{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e994fd2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import transbigdata as tbd\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cfe98cdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>VehicleNum</th>\n",
       "      <th>Time</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Lat</th>\n",
       "      <th>OpenStatus</th>\n",
       "      <th>Speed</th>\n",
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       "      <th>3</th>\n",
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       "      <td>20:22:07</td>\n",
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      "text/plain": [
       "   VehicleNum      Time         Lng        Lat  OpenStatus  Speed\n",
       "0       34745  20:27:43  113.806847  22.623249           1     27\n",
       "1       34745  20:24:07  113.809898  22.627399           0      0\n",
       "2       34745  20:24:27  113.809898  22.627399           0      0\n",
       "3       34745  20:22:07  113.811348  22.628067           0      0\n",
       "4       34745  20:10:06  113.819885  22.647800           0     54"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('D:/code_for_school/visualtaxi/gps_data/shenzhen_taxi_gps.csv', header=None)\n",
    "data.columns = ['VehicleNum', 'Time', 'Lng', 'Lat', 'OpenStatus', 'Speed']\n",
    "data.head()                 #head函数其中一个默认参数为5，故只返回头5条数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "27df3a06",
   "metadata": {},
   "outputs": [
    {
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       "      <td>21:19:12</td>\n",
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       "<p>544999 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "        VehicleNum      Time         Lng        Lat  OpenStatus  Speed\n",
       "0            34745  20:27:43  113.806847  22.623249           1     27\n",
       "1            34745  20:24:07  113.809898  22.627399           0      0\n",
       "2            34745  20:24:27  113.809898  22.627399           0      0\n",
       "3            34745  20:22:07  113.811348  22.628067           0      0\n",
       "4            34745  20:10:06  113.819885  22.647800           0     54\n",
       "...            ...       ...         ...        ...         ...    ...\n",
       "544994       28265  21:35:13  114.321503  22.709499           0     18\n",
       "544995       28265  09:08:02  114.322701  22.681700           0      0\n",
       "544996       28265  09:14:31  114.336700  22.690100           0      0\n",
       "544997       28265  21:19:12  114.352600  22.728399           0      0\n",
       "544998       28265  19:08:06  114.137703  22.621700           0      0\n",
       "\n",
       "[544999 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1724017e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114.143157</td>\n",
       "      <td>22.577605</td>\n",
       "      <td>罗湖</td>\n",
       "      <td>POLYGON ((114.10006 22.53431, 114.10083 22.534...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114.041535</td>\n",
       "      <td>22.546180</td>\n",
       "      <td>福田</td>\n",
       "      <td>POLYGON ((113.98578 22.51348, 114.00553 22.513...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114.270206</td>\n",
       "      <td>22.596432</td>\n",
       "      <td>盐田</td>\n",
       "      <td>POLYGON ((114.19799 22.55673, 114.19817 22.556...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>113.851387</td>\n",
       "      <td>22.679120</td>\n",
       "      <td>宝安</td>\n",
       "      <td>MULTIPOLYGON (((113.81831 22.54676, 113.81948 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>113.926290</td>\n",
       "      <td>22.766157</td>\n",
       "      <td>光明</td>\n",
       "      <td>POLYGON ((113.99768 22.76643, 113.99704 22.766...</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   centroid_x  centroid_y  qh  \\\n",
       "0  114.143157   22.577605  罗湖   \n",
       "1  114.041535   22.546180  福田   \n",
       "2  114.270206   22.596432  盐田   \n",
       "3  113.851387   22.679120  宝安   \n",
       "4  113.926290   22.766157  光明   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((114.10006 22.53431, 114.10083 22.534...  \n",
       "1  POLYGON ((113.98578 22.51348, 114.00553 22.513...  \n",
       "2  POLYGON ((114.19799 22.55673, 114.19817 22.556...  \n",
       "3  MULTIPOLYGON (((113.81831 22.54676, 113.81948 ...  \n",
       "4  POLYGON ((113.99768 22.76643, 113.99704 22.766...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the GeoDataFrame of the study area\n",
    "sz = gpd.read_file(r'D:/code_for_school/visualtaxi/geo_data/sz.json')\n",
    "sz.crs = None\n",
    "sz.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "71dda92b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>海珠区</td>\n",
       "      <td>440105</td>\n",
       "      <td>MULTIPOLYGON (((113.38326 23.10976, 113.40735 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>番禺区</td>\n",
       "      <td>440113</td>\n",
       "      <td>MULTIPOLYGON (((113.39735 23.08384, 113.40207 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>白云区</td>\n",
       "      <td>440111</td>\n",
       "      <td>MULTIPOLYGON (((113.46203 23.39910, 113.46264 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>从化市</td>\n",
       "      <td>440184</td>\n",
       "      <td>MULTIPOLYGON (((114.00607 23.93356, 114.02272 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>花都区</td>\n",
       "      <td>440114</td>\n",
       "      <td>MULTIPOLYGON (((112.96734 23.44384, 112.96392 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
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       "  name      id                                           geometry\n",
       "0  海珠区  440105  MULTIPOLYGON (((113.38326 23.10976, 113.40735 ...\n",
       "1  番禺区  440113  MULTIPOLYGON (((113.39735 23.08384, 113.40207 ...\n",
       "2  白云区  440111  MULTIPOLYGON (((113.46203 23.39910, 113.46264 ...\n",
       "3  从化市  440184  MULTIPOLYGON (((114.00607 23.93356, 114.02272 ...\n",
       "4  花都区  440114  MULTIPOLYGON (((112.96734 23.44384, 112.96392 ..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the GeoDataFrame of the study area广州的地图\n",
    "gz = gpd.read_file(r'D:/code_for_school/visualtaxi/geo_data/gz.json')\n",
    "gz.crs = None\n",
    "gz.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "036701a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C+RoKqRD/99BEnw9mgMKZcNChEteEMwB8fboOD390DBovq/Sm0Zvx12+K2G4GpwwNl2F79jRMHdb3fqS+hMKZcArDMDh4qdGl59xdVI85/9iPM9Ual57Xnd78vhhNOhPbzeCMmcPDsS17GhLDZGw3xWMonAmndD7Mc31NiaqWDtyx7iA2H6ng/NZnp6s0+Di/nO1mcMYj05Ow4QHvW+E3WPRAkHCKq8abe2Oy2LBy62m0GSx4ZMYQl5+/XmuAOlgyqHPYbAxe3H4GHP/54RFCAQ+v/XoUFk6IY7sprKBwJpxhsdrw2bFKt1/nr98WYXSsApOGhA76XDqjBV+erMHmo5UorGzFkzOH4pms4RDwnVtA899jnefxd6EyEf61eDwm+PF0Qgpnwhnv7y/FySr3jwtbbQyWfnoCO5dlOtXTZRgGJ6s02HK0AjsKa6C7atfstXsv4VytFu8sGguF1LFfw1t0JrzxLT0EHBEVjPeXjPfKehiuROFMOKG4rg1v55732PUa2oxY+ukJbHp4kt07YWj0Zmw7UYXNRytRVNfW53F7ihvw63/+iPVLJmCYOsjuNv3tu2K/3z/wprQIvLXwOsjEFE30X4Cwzmy14fefnYTJavPodY+UNmP1d8VYdeuIXt9nGAYljTocutSEg5casetcPUwW+9pY0qjDr//5I95ZdB1mjYgY8PjCylZsPuqfO28rpELMHB6OG0dG4Nb0KPCdHBLyNRTOhHXv7b2E0yxNc/vXvhKMjVfi5vRIMAyDsiY9Dl1qwuGSzq/6NudnjrQZLXj4o2NYMTsFS381rM9CTlYbgxe/8K+HgElhMsweocasERGYkKDk7D5+bKJwJqw6W6PF/9t9gdU2PPvZSXxzphb5Jc2o0xpcem6GAf6eex5na7V4c8GYXn9d//RIBWs/nDzt6dkpuG1MFIaG2z/c468onAlrTBYbnvnsJMxWdruMbUYLthfWuPUa35ypQ2mjDusXT0B86C8PuprajVj9XbFbr80V4+JDsHx2MtvN8Br0uwRhzZrdF3CuVst2MzymqK4N8/55AAcu/LIC8o1vi6Dp8I+HgA9Pd/3ccl9G4UxYcbpKg3/uvcR2MzyuVW/Gkv/k49/7S3C8vBn/PVbFdpM8Ik4lxU1pkWw3w6vQsAbxOKPFimc+K4TVT0uu2Rjg1Z3nIBP5x6YBAPCbqUlOL8zxV9RzJh73zq4LOH+5ne1msO7qxSu+TC7p3N+POIbCmXjUiYoW/CvP/4Yz/FUAn4c/356GIFpU4jD6L0Y8xmC24pnPTlIBeT8hlwTgvfvHY5qf1F92NQpn4nYWqw37LjTgPwfKUNKgY7s5xANiQqT44DcZPrcjtidROBO3KarT4vPjVdh2ogaN7a6v0Uy4aUysAv9+IAPhcjHbTfFqFM7EpRiGwf8dLsd/j1XiTLX/zGEmnW5Ki8A7d4+F1I9morgLhTNxqU/yK/Cn7T+x3QzCgnsmxuHV+aNoypyL0GwN4jKljTq8tvMc280gLNl3vhGXGmiKpKtQOBOXsFhteOa/hegw+8fcXdJTdWsH7lx3ED9edO0Gvf6KhjWIS/xrXwkKKlrZbgZhWZvBghe/OIMRUcEYHinv/IqQI14VSHWaHUThTAbtREWLR3cxIdwjEvAQHSKFMlCIWo0RO0/XYufp2q73pUIBUiKCkBIh7wrt1MhgmtHRD4eGNXJycpCRkQG5XA61Wo358+ejuLh7ucOXX34ZqampkMlkUCqVmD17NvLz8wc8d2trK7KzsxEVFQWJRIIRI0bg66+/duxuiMfYbAwqm/X49/4S3P2vw7DQyhK/EB4kRlq0HBmJSmQkKpEWHYyIYDFM1s6NCk5Uanqtid1htuJklQafHa/CqzvPYfGGI1jx30LP34AXcajnnJeXh+zsbGRkZMBiseD5559HVlYWzp49C5lMBgBISUnBmjVrMGTIEHR0dODtt99GVlYWLl68iPDw8F7PazKZcOONN0KtVuN///sfYmNjUVlZCbmcJrBzgd5kwYmKVhTXtXV+XW7Dhctt0JmsSFYHwcZ4dnsp4l5SIR9RCikUgUIIBXyYrTZo9GbUagxoaDeiwUVz1mNCpC45j6/iMYzzm+M0NDRArVYjLy8PM2bM6PUYrVYLhUKBXbt2YdasWb0e895772H16tUoKiqCUOjYjsXXXkej0SA4ONipc5DuGIbBjpM1yPm6qN8dQoaEy6AMFOJUpQZm6kF7NQEPUMpEaGw3uf1av89KwdJf+V/xfXuzalCzNTSazq11VCpVr++bTCasX78eCoUCY8aM6fM8O3bswJQpU5CdnY2IiAikp6fj9ddfh9Xa95N/o9EIrVbb7Yu4ztkaLe7+12Es31w44NZNJQ06HC9vRaA4AJOSVFDTOKLXui5O6ZFgBoBYZeDAB/kxpx8IMgyDFStWIDMzE+np6d3e++qrr7Bo0SLo9XpERUUhNzcXYWF9Fz8pKSnB7t27cd999+Hrr7/GhQsXkJ2dDYvFgj/96U+9fiYnJwevvPKKs80nfWjRmfBW7nl8kl/ucIEiTYcZ+aXN4PM6l/BabDb8VNPmnoYSt2jWeyaYASBWScMa/XF6WCM7Oxs7d+7EgQMHEBsb2+09nU6H2tpaNDY24v3338fu3buRn58PtVrd67lSUlJgMBhQWloKgaBz2edbb72F1atXo7a2ttfPGI1GGI2/jH1ptVrExcXRsIaTrDYGnx6pwJvfF6NV77ptk+JUUkQFS3CmWgO9mcamuWxkVDDOenDbsMOrZiFSIfHY9bjC3mENp3rOy5Ytw44dO7Bv374ewQwAMpkMw4YNw7BhwzB58mQkJydjw4YNWLVqVa/ni4qKglAo7ApmABgxYgTq6upgMpkgEol6fEYsFkMspl+fXeFoWTNe2v6TW74xK5s7UNncgUCRAOPjQ3Cc5kJzVoDAc/OQhQIeDX8NwKFwZhgGy5Ytw7Zt27B3714kJSXZ/bmre7nXmjZtGjZt2gSbzQY+v3MY/Pz584iKiuo1mIlr1GkM+Os35/CFm3eeBgC9yQqBgBakclWsUopTVRqPXS86REqLUgbg0HdLdnY2Pv74Y2zatAlyuRx1dXWoq6tDR0cHgM7hjD/+8Y84fPgwysvLUVBQgIcffhhVVVVYsGBB13mWLFnSrRf9xBNPoKmpCcuXL8f58+exc+dOvP7668jOznbRbZKrGS1WrN17Eb/6+16PBPPV1yXcFOXh4QUabx6YQz3ndevWAQBmzpzZ7fWNGzfiwQcfhEAgQFFRET788EM0NjYiNDQUGRkZ2L9/P9LS0rqOr6io6OohA0BcXBy+//57PP300xg9ejRiYmKwfPlyPPfcc4O4NdKbVr0Jv157EKWNni96326wePyaZGAKaYBHe80AEBtCMzUG4vCwRn8kEgm2bt064Hn27t3b47UpU6bg8OHDjjSHOEEiFLASzAAQKApAkFiAdiP1oLkkNTIY+aXNHr0m9ZwHRoOAfkYiFCAsiJ0HMaerNQiWChGvom9MrhDyebhQ7/npjoWVrbDRgqV+UTj7ITZ7LTWtBjS0mzAmTsFaG8gvRseFoFnnuqmT9vqhqB7/b/cFj1/Xm1A4+6EYln+l7DBZcbJSg0lJva8sJZ7D5t6O7+y6gNyzl1m7PtdROPshroz35Zc2Y1x8CNvN8Fvp0cEob9Kz2oantxTiYj3tntIbCmc/xKWaBgUVrZhIPWh2cGCacbvRgkf/7xi0Bs8PrXAdhbMf4krP+YpjZc1Ii6Yl954Ur5JyZnf0kgYdVmwppAeE16CdUPzQQY7t8WZjgLO1WoQHiaGSiRAkDkCAgAcbw0BvsqJVb8JlrQEWKs3hMqEyMSqaO9huRpdd5+rx7g8X8PSNKWw3hTMonP3Mxfo2bPyxjO1m9MAw6LeQO48HhMuvhLcAAXw+rDYGHWYLWvRm1FN42214hBwnKlvZbkYP7/5wAWnRwchKi2S7KZxA4exHGIbBK1+e9cotpRgGaGgzoqHN/vBuN1rwUw03fnXnEpvz+2u43Yr/nsQX2UEYpg5iuymsozFnP/L92cvYf4FbQxquciW8i+vacLy8FfmlzZCJqe9xrbHxIbjA4dkR7UYLHv2IHhACFM5+w2C24i9fnWW7GYRlta3972rDBSWNOjy9mR4QUjj7ifX7SlDVwp0HQJ5goip4Pbhqc1Z3+6GoHu/84N8rCCmc/UB1awfW7r3IdjM8TttBVfCupZA6t4EyG/7fDxfw3U91bDeDNRTOPkzTYca/95dg0fpDMPjhFlGNOu/oJXqKMlA4YGVJrlmxpRAXWSjMxAX0xMQHnavV4qND5fjiRDU6zP75q32gSEA956skhcmgM1pQ38dsF67Smax4cONR/PWO0chM7nuTaF9E4cxRWoMZ7+ReQN75esQqAxGrlF71v1LEKKUIDxKDx+tcg2u22vD9T5fx4aEyHPFwbV4uUsvFKGO5bgRXjIlT4Hxdu9f+oK5q6cD9G/JxU1oEXpgzEnEq7pQfcCcKZ46x2Rj873gV/vZdERrbO7epv9TQe3F8cQAfMUopYkKkOH+5DZe13tUrcqdgLxpbdadJSSqPF9J3l+9+uoy9xQ147PqheOL6oZCKBAN/yItROHNIYWUrXtp+Bift3DLIaLGhpEGHkj7C25+JA/z7cYpIwENajMJngvkKo8WG//fDBXx+vArPzxmBW9Iju3579DUUzhxwplqDDQdKse1ENdtN8Rl8H/2GtUdYkAgKqRAnKlrZborbVLd24MlPCjB1aChenpeGlAg5201yOQpnlpgsNnxzphYfHSrH8fIWtpvjc4x+WmhjWLgMLXpzn0NhvubgpSbc8u5+LJmSgKdmp3jVVMGBUDh7mNZgxr/3l2JTfgWru1D4OovV/8J5XHwIztRoYfKzH0xWG4ONP5ZhR2EN/nDzcPx6bCxEPjCsxWO8beJjH7RaLRQKBTQaDYKDuVcb2GZj8HlBFd74tphC2QN4PGBUjAI2G4MzflD8aGKSimbp/CxIHIBpw0Ixc7gaM4eHI0rBrfrl9mYVhbMHnKxsxUs7fkIhB8s0+oMYpRQxCinO1mjQbvLO6WR9kQr5SI6Q45SdD5H9UWqkHNcPD8fMFDUmJCohFLDbq6Zw5oA6jQFv5Rbjv8eq2G4KARCvCkRFs+/MfY6QiyEVCWg+twOGqYPw38emQCUTsdYGe7OKxpxdxGix4qcaLQorWnGishWFlS2o5NBOEwSoaNYjVCZCk87EdlMGLTVSjlqNAZe9bMUfmyYkKLH2/nGsBrMjKJydwDAMKpr1KKxsxYmfw/hcjRYmP3wI5W0SQgO9PpwzEpU4UdHqlZsmsOX+yfH4021pXvWgkMLZDpoOM05VdQZxYWXnV7OXf4P7K2+d/ywO4GF0bAhqWg04WkZTL+0lEvDxl/lpuDsjnu2mOIzC+RoWqw3Fl9u6BfFFDu8cQRzjbWPOKpkIyeognKvVUig7KCJYjHX3j8e4eCXbTXEKhfNVNHozbv/nAXrA4sPq24yIDpGghuM7giSoAhEmF+NUVavPLcH2hCvjy2q5hO2mOM17BmA8YPX3RRTMfiAmhFvzXq81IVGJqtYOHC9vgdlK48qOyhwWhk2PTPbqYAYonLtp1dOmkv6Ayw/SJiWpcKysBVYOt5HrDl5qxAcHS71uY4FrUTj/jGEYePdfJbFXaSP36k6IBDxcF+d7VeTYYGOA178uwvLNhejw4kVHfh/OJytb8eIXZ3D96r1YOD4WQ8NlbDeJuFmr3ozEUO4UbFcGCpEQKkNhJa3yc6UdJ2twx7qDqPSyh8BX+H04R4VIcPBSI0wWG2akhGPrE9MwZUgo280ibsaV8cg4pRTiAAEu0IwgtzhXq8XcNQdw4EIj201xmN+Hs1ouwaePTsZDmYng8XhQBArx4UMTMW0YBbQvM3Bgy6aRUcFo1ZtRp+X2zBFv16o3Y8l/8vH+vhKvGof2+3AGOgP60RlDu/4sCuDjpblp4HvnegVih4sN7az+/Y5PUOL85Ta0GWkTWk+wMcBrX5/Dyzt+YrspdqNw7kNKhByLJnrfqiJiH5PFhtGxIYgOkXT78sT2VpOSVDhe3sLpWSO+6uP8CtRqvKPmDYVzP56enQKZj28i6Q8iFRKMiVVgUpIKY+NCkBAqBcMwKKxsRU2rodtXsjrIbe0Q8nkYFx9CMzJYZLUx2JRfwXYz7ELh3I9wuRhPzBw68IHErQYz/DAxSYU6jQEnqzTIL23GicpWlDd1wNNrO4KlARiiDkKBD+/r5y0+PVIBo4X9Zw4DcSicc3JykJGRAblcDrVajfnz56O4uLjbMS+//DJSU1Mhk8mgVCoxe/Zs5Ofn232NzZs3g8fjYf78+Y40zW1+mzkEUQpuPNn3J6EyESYlqRATIsVYJ2sjpEUH46gDvdRh6iAESYQYFx8CVaDr9qITCnhQSIUormtz2TmJ8xrbTfjmdB3bzRiQQ+Gcl5eH7OxsHD58GLm5ubBYLMjKyoJO98uk/pSUFKxZswanT5/GgQMHkJiYiKysLDQ0NAx4/vLycvz+97/H9OnTHb8TN5GKBFjkhRWtvJGAz8OYWAVGxyjQojchv7QZ1T8vY85IdCygh4TJUNGst2thkUomwvgEJS7Wt+NwSRMKKlrRrDcjXhWIUTGD37hhbLySantzzH9+LOX8XouD2gmloaEBarUaeXl5mDFjRq/HXKn6v2vXLsyaNavPc1mtVlx//fX4zW9+g/3796O1tRVffPGF3W1x104oWwuq8Nznp6jGgRvFqaSIUkhw8XI7mvtYQi/gAckRchT10/sU8HkYHaOAwWzFOTt6qQF8YFyCCqerNX2uJFNIhRDw0Ge7BqKQBsBiZaDz4pVqviojUYm1941HuFzs0evam1WDGnPWaDpXNKlUql7fN5lMWL9+PRQKBcaMGdPvuf785z8jPDwcv/3tb+26ttFohFar7fblSgzD4O3c81jx35MUzG4QKBJgQoISyRFBqGzuwJHSln4D0MoAl7WGXr+RVDIhJiWpoAoUdW58YEcwj45RQB0swZHS5n6X+Go6zIhVOr+aMCVCTsHMUUfLWjD3Hwc4u7en0yVDGYbBihUrkJmZifT09G7vffXVV1i0aBH0ej2ioqKQm5uLsLCwPs/1448/YsOGDSgsLLT7+jk5OXjllVecbX6/jBYrVn1+GltPVLvl/P4qVCZCUpgMFhuDolotjpU7Vp+4RW/GMLUMmg4zTBYbUiKCECQOcKqs5rk6rd0/dE9VazApSeXwNWKVUnoAyHF1WgOOlTXjurgQtpvSg9PhvHTpUpw6dQoHDhzo8d4NN9yAwsJCNDY24v3338fChQuRn58PtVrd49i2tjbcf//9eP/99/sN8GutWrUKK1as6PqzVqtFXFycczdzFY3ejEf/7xhNd3IBAZ+HZHUQgqVCNLQZUdqoG/QWURfrdUiPCUaHyYrzl51f8iwTBzhUhTC/tBkpEUHQm6yoarFv/FglE9l9LGHHMHUQHpiayHYzeuXUmPOyZcvwxRdfYN++fUhKShrw+OTkZDz00ENYtWpVj/cKCwsxduxYCAS/zCe22ToH6vl8PoqLizF06MDT2Vw15lzT2oHMN3aD1gc4Ry4JwPAIOSw2Bhcvt6Gdo7/SRyokqNM4vmxaKOBhXLwSR8ua+/03MkwdRDvoeIH/++1ETE8O9+g13bL7NsMwWLZsGbZt24a9e/faFcxXPmc09r5LcGpqKk6fPt3ttRdeeAFtbW149913XdIbdkR0iBS/So3ArnOXPXpdb8fjde4+UVTX5vBwBRuMTtbWMFsZ5Jc2Iymss3phX+VHvWWHZ392U1qEx4PZEQ6Fc3Z2NjZt2oTt27dDLpejrq5zrqBCoYBUKoVOp8Nrr72GefPmISoqCk1NTVi7di2qqqqwYMGCrvMsWbIEMTExyMnJgUQi6TFmHRISAgA9XveU+ybHUzg7IDVSDqPF5lV73LUPsqZFaaMOAj4Pk5JUKChvgfmabnRtKw1ncJk4gI8X5oxkuxn9ciic161bBwCYOXNmt9c3btyIBx98EAKBAEVFRfjwww/R2NiI0NBQZGRkYP/+/UhLS+s6vqKiAnw+dxcnzkgOR0yIFNX0DdavsCAR4lWBfvvQy2rr7EVHKySIUwVCb7LgYn07VDIRKmmsmdMeu34o4lTcqendm0HNc+YSV89z/ueei1j9XfHAB/qhK+Oup6pa0WHm9kT+vigDhWhxw7ZkAj4PKRFB0BmtXrfTtz/Znj0NY1iaoeGRec6+bMGEWAR4Sc1QAZ+HtOhgKKSuW3Lcl1ExCoTLxcgvbfbaYAaAQJF7Np632hicq21DbWsHJiYpwfOOf0J+5+Uvf4KN40/9KZz7oJZLcFNapFOfjVN5dnfnCLkYP9VooekwIyxIhFExwV0V2Fy103RMiBSjYoJxulqDmlbvLw4vdXO1QbONwZHSFgyPkCMi2LMr0MjATlS04uP8crab0S8K537cN8mxmhpp0cHY9MgkRCs8G87Kq2YGNLabcLpa21WBrbq1A1IhH8Mj5JiYqEJGotLh/fNiQqS4rO3A6WrXrsJkkyfqNgNAUV0b2g0WTEhwrngTcZ+/fVvM6drOFM79mDI0FEPCBt7wNTJYgr8vGIMvl2ai3WDx+AKWgXqBHWYbii+34UhZM46WtaBW0wG52P5f6/UmCzheI8ZhnnzSojNZcay8BWNiFQhxYbU7MjjtRgte/OInzm5dReHcDx6Ph3v76T0HigR45sYU7Pn9TNw5PhZWhsFfvynyYAs7CRwc2DRaGIyItv+haYveDHGAbw2eslHP92SVBgkc2vWbALvOXcY3Z7hZPtQ9T0V8yJIpibghVY1mnanbl8liw32T47vt4vzJ4XKU9LEowZ2c2e6oqsX+mQThcjEsVhuMFtfPbmCLgaWHmVQ61HF3T4hD8eU2nKvVwuiGX+Fe2vETpg0Ng4Jjv9VQOA9AFMDH0PAgDB1gIZGmw4x3f7jgmUZdQ2d0PDRrWg0YFaPA6WpNn8eEBYkwJEyGExWtPRZZeDudyfMbq0YGi1Gn7X2lLOldWJAYb9w1GgBgttpwsb4dZ6o1OFOtwelqDc7Wagf9g7ahzYh/7buEP9yc6oomuwyFs4v8c89Ft8ybtUdTu7PX7T1wVTIRhoUH4URlC4540ao/R+hY2PU6OkRK4eygtKuG34QCPkZEBWNEVDAWTOgs62Cx2lDSqMPpqs6w/qmm838dCewxsQo8Mn2Iy9s+WBTOLlDZrMcHP5axcm2hgIeGdue+4U9XaxGtkKDm5wJAKpkQw8LlKKxswZEy363KJxTwWKnR3aI3Q8Dnwepjv4W408gBno0ECPhIiZAjJUKOO8fHAgDO1mhx9/pDaDPY9wP4j7eO6DbjiSvogaAL/PXbIpis7IxhDnYXh1hlIEIChZiYpITOaMGRsmaYfHxzAbmYnbHF0kYdxtOUOoekOfDg+oqR0cH4z4MZkAgHjrdQmQgTEnvfLIRt1HMeJJ3RgvwS9nqZykDRoBaFnKpqBQAcKfXN4Yve6E0WjI9Xgs/vnJEDBrAyNpitDEwWG4IkATjmpuGcExUtiFVKqc6zndKiFU59LiNRhXX3j8cjHx7r94H57BEREHB0JTCF8yDJxAHY9uRU/OaDo6zU75UKB7fSzeBrE5jtYLDYcLyi7/CVSwIgFwegzQ3j0mYrA5mblo77miBxABIGUZzohuFq/H3hGDy1pbDPee03pUc4fX53o2ENF4hTBeLzJ6ZiIgu/HnH1p743azNYBhzrHIziy22Y4OBu4v5oRJQc/EH++779uhj8eV5ar+/JRAJMHWr/7kueRuHsIgqpENOGef4v2sLSWLevK6hogdqNuzLzQT9UB+LskMa1Fk9JxDM3pvR4fWaqGpJB/ubpThTOLtSs8/w0qXYjN7eB8nZmK+PWer+tHYPbS9EfuPK3l6W/GoaHpnXfucnZwmaeQoNfLtTshnnOI6OC8Zf5adCbrMgvaUZ+aRNOVmq6Zoc0D3LDVNK34lqtW6a+BfD73t6K/MKZmRp94fF4eGHOCGg6zPi8oAoiAR83DOfuFlUAhbNLubLnLBLwsXx2Mh6dMQRCQecvOFf2OzOYrThR0Yr8kias33fJZdck3aXFKNxSxCpOJaNwHoBQwEOyWu7Sc/L5PLxx5yhoOsyw2GyQS7i1XPtaFM4uZHRRvYZx8SH4212jMayPf5wSoQBThoZiytBQRCul+MP/TrnkuuQXKpkIJ3+eZuhq9VoDeDzPVsbzNslqOURuKOsaIOBjzb1j8VMN98vf0pizCz2Uad9u5L0RBfAxd0w0PvhNBj57fGqfwXytBeNjMf+6aKevS3o3JFzmtuJIOpMV8Rzfv45trhzSuJZEKPCKxUDUc3ahW9IjMSFBiWPl9i9gGBsfgrvGx+K20dFObTPF4/Hw6q9H4WSVhn5VdpFYpRQFDvwdOkMtF6O8ifYY7Is7w9lbUM/ZhXg8Hp6fM2LA4yKDJXhy5lD88Mz12PbkNNw3KWFQ+/8FiQPwj3vGQiSgv05XCA8Sw93lL6i8Rv8mDw1luwmso+9mFxsbr8TcMX0PMwwJl+HHlb/CH25OxdDwIJddNz1GgVW3cqvkobc6UdmKcfEhEArcNxeZZtn0bc6oKKRGUs+ZwtkN/nDT8D5X7pU36d22C8eDUxNx40juLkf1JgUVrUhWB0Hmpo1gQzywU7o3EvB5WJHVc8GIP6JwdoM4VSCGhve+96DVxuBkZd8F7geDx+Nh9V2jEa2QDHwwGdDZ2jaEy8UIdUM5SaGHNpj1NneNi3Xpb5TejP6FuEl/v5YV9FN0Z7BCAkV4956xVHPDRQR8nluW+DY5WYPbl4kCOuf2k04Uzm4yIqqfcHbzTICMRBWW/WqYW6/BdRMTlVANYk84HoBJSSqUN+lR3er68p5s7ZrDZZHBkkFXWfQlFM5uMiKq73nKBRUtbt+OfXiEa1dXeRO5JACnq7VQykSQ2lFw/VpRCgmSI4KQX9rs1Oa59ogNkbrlvN6solmPBf86hFoN1boGKJzdZmQ/PWdNh9ntxdYva50vwO/tRkYFo8NsxaUGHYaGB8GRSRcTEpRo7TDj/GX31uaWuulBo7e7WN+Ou9Ydcmh3eF9Fi1DcJFwuRqxSipBA4c+7dwdhmLrzfxPDAiEOcO835+U2/xzTFAp4uFDf1vXnMzVajE9Q4vgAQ0khgUIkqAIdWkA0GJoOGtboS3VrB97OvYC/LxzDdlNYReHsJjweD/v/cEPnNkgs8Nee83VxITh6zRZTx8tbMDFJhSN9FDEaHaNAVaseJ6vcM4umN1XN1DPsz/bCaqzISkGMHw//0LCGG7EVzABQr/WfnvOEBCVSIoKQHhOMSw29L2E/UtqMjGt2H5EE8JGRqMSpag2adZ7tyabFuKaQvK+y2Bi8v6+E7WawisLZR/lLzzkjsbOWyfnL7ThTre135d2x8hZcFxsCABgWLkNokLhHL9sT4pRSt06n9BWbj1b49ZRDCmcf5SvhHKeUYkKCEhMSlIhXBSIyWIKgnx+mTUxSORSuDAP8VKtBRqISpW6aImcPqUgAs5WKawzEYLbhg4NlbDeDNTTm7IM6TFZoDa7fOdrdpEI+ksJkkEuE0JusKGvSobKlA5XXzGwZHx+CAAHfqUL4ZivDSm/5ioxEJavX9zYfHizDozOGcL4wvjtQOPsgvckCoYDnVb2z9JhgnK3R4mxt24DHnq1tQ4fZ+/ZOjAwW40w194u8c4nWYMGm/Ao8dv1QtpvicTSs4YNCg8R4bf4otpvhkEBRgM+X0VTJxF75Q4VtX56qYbsJrKBw9lELM+Lw8CB2ZvG0Bh+flz0hQYmztdRrdsb9kxLYbgIrKJx92KpbR2BWqprtZgxIIQ3w+V1ctAZadOKMIWEy3DU+lu1msMKhcM7JyUFGRgbkcjnUajXmz5+P4uLibse8/PLLSE1NhUwmg1KpxOzZs5Gfn9/ved9//31Mnz4dSqWy6zNHjhxx/G5INwI+D+vuH49FGXFsN6VfSWG+XSJSyOf5/A8fd3kmazgC/HSHH4fuOi8vD9nZ2Th8+DByc3NhsViQlZUFne6Xf3gpKSlYs2YNTp8+jQMHDiAxMRFZWVloaGjo87x79+7FPffcgz179uDQoUOIj49HVlYWqqurnb8zAqCzDGPOHaPwp9tGgqtVREVu3HGECxLDZV71cJYr0mOCcUt6JNvNYA2PGUR5tIaGBqjVauTl5WHGjBm9HqPVaqFQKLBr1y7MmjXLrvNarVYolUqsWbMGS5YsseszV66j0WgQHExb3PRmb3E9lm06gTYjt6bZxSmlPabL9UcqFHjVg7WJiSocKXN82p+/++ihiZiREs52M1zO3qwa1FQ6jaazFoFKper1fZPJhPXr10OhUGDMGPuLmOj1epjN5j7PCwBGoxFG4y8PkbRaetgykJnD1diWPRUPbjzq9qp49lLJRA4FMwCYrLYeS7F5PF6ftTPYZrLa2G6C15k8RIXpyWFsN4NVTg/mMAyDFStWIDMzE+np6d3e++qrrxAUFASJRIK3334bubm5CAuz/z/0ypUrERMTg9mzZ/d5TE5ODhQKRddXXBy3x1W5YphajttG970BraclhgY6/BmrrXMhydVf5y8PPD+aLXU+slrTk/5wcyqrtWm4wOlwXrp0KU6dOoVPP/20x3s33HADCgsLcfDgQdx8881YuHAh6uvr7Trv3/72N3z66afYunUrJJK+98JbtWoVNBpN11dlZaWzt+J3IoLFbDehC5+rA+EupAp0/R6EvuzGkREYF68c+EAf51Q4L1u2DDt27MCePXsQG9tzmotMJsOwYcMwefJkbNiwAQEBAdiwYcOA533zzTfx+uuv4/vvv8fo0aP7PVYsFiM4OLjbF7FPRDA3NoAVCnio9IPSmXIJLcR1RLLat2fv2MuhfzUMw2DZsmXYtm0b9u7di6Qk+xY5MAzTbXy4N6tXr8arr76K7777DhMmTHCkWcRBXOg5DwmTwWJjUOEH4aw3cesBLNet3XsJfB4Pz2Sl+PXQhkPhnJ2djU2bNmH79u2Qy+Woq6sDACgUCkilUuh0Orz22muYN28eoqKi0NTUhLVr16KqqgoLFizoOs+SJUsQExODnJwcAJ1DGS+++CI2bdqExMTErvMGBQUhKIh+irqaWs5ez5mHzmpyx8tbXLY/X6hMhOGRQTBbGZyt0UJn4tZMjrIm3/8B5Gpr9lxEi96EP9+e7rc7yTs0rLFu3TpoNBrMnDkTUVFRXV9btmwBAAgEAhQVFeHOO+9ESkoKbrvtNjQ0NGD//v1IS0vrOk9FRQVqa2u7/rx27VqYTCbcdddd3c775ptvuug2ydXaWZpKFxMiwTC16zZOFQp4yEhUokVvwtnaNhwta4GVYTApqe9ZPmyQSwKgkPpfVbXB+iS/Ass3n4DJ4p+zXQY1z5lLaJ6zfdia65yRqMTpag0MZtd+o/F4QHq0AhIhHwwArQc2Z7WHTCTAiKhgtBktKK7j7kwSrhMJ+PjrnaNwxzjfWcLtkXnOxHswDIP//FiG13aetbv6W5RCglrN4KaBqQJFiA6RuK2GMcMAp6s759unRspZD+bUSDlkogCcqdF4bLNYXxQoEuC+SfF4ePoQzjzA9jQKZz9gstjwp+1nsPmo49MNx8WHoKRRh1a944V7eADC5CKcqfHMAiEuDE1e1hrQ4sR/K9JJGSjEb6YlYcmUBIT4+RRECmcf16wz4fGPjzu8ei5CLkatxoBajQEKqRDj4kNQUNFq12djQqSIUkggEwcg73zfNVVc7WxtG1Ij5ShiaRhByOdRMDspSiHBI9OHYNHEOASKKJYACmefdv5yG3774VFUNju+VDtOFYjLP9dY1nSYUVDRitExCtRoOtDY3n0T1XiVFBHBks6pcT/vzVfd2oEEleOr/waLzZlXYT//QCP2GxIuw+PXD8X862IgCvDP6nN9oXD2UScrW3Hfv/OdnpnB7+X75FS1BjKRABMTO2dDmKxWlDXpUdHcgYprfgAMUwfhYr3nx3/P1bZhRKQc51joPSukQgpnO42KUeDJmUORlRbpt1PlBkLh7KPGxIXgm+XTsbuoHruL6nGopKnHlKRh6iAESwJwoqIVV54Rjo5VoN1owdkaba9DGTqT1a4KayEsTh0b6HlnqEyE+NBABPB54PN4Tm0U2xsZ/TpulzX3jsWcUVF+vcDEHvSvyYfFqQLxwNREPDA1EXqTBQcvNuGHonrsKapHndaAAD4PBRWtiA6RIFYZiMY2I05Vabo+P5i5yEYLewtBiuraMClJhePlzbDYALVcjFilFHweD7WaDlS3GtCk+2VoxlXj1AE+XpfaFWJCpBTMdqJw9hOBogDMHhmB2SMjwDAMvj1Tiyc+OQEAqGk1oKa156/jUqHA6etJRc5/1hXyS5uRHh2MxnYj6rRG1PezR6Gmw+RUjehAkQD6q1YjWr1kyUCwJABaAzsLkW5Ki6RgthOFsx/i8Xioahl4bHQwBe1tHFjUFSgOQJ0d0/hqNUZkJCodmos9NFyG6pYOTExU4WJDO5p1Jhg5vAFAkDgAd42PxQNTE5EUJkObwYzq1g5UNXegqkWPqpaOzq/Wzv/vzNRJe9zsxzubOIrC2U/ZM248mJ6zpoP9KWUGB8LyaFkLRkYF271DtlDAh8Fiw5GyZogEPExMVPbbO2dLYmjn0NZd42Mhl/zyHEAuESI1UojUyN5XqLkjvENlIoxPoFKg9qJw9kM2G4OjdoSzbhBLvKtb2d1phc8DShycLdLUbuwxVNGb6+IUKKz8ZWzeZGVwpKwFsUqpU211h+nJYfjNtETMTFE7VTPbHeF948gImpnhAApnP3Shvt2uns+lhnYI+DxYnXgwqDdZoZaLWetNDgl3fCrf5Tb7hjfKm/QYHauAVCiA3mRBdUsHmjmw+EQqFODO8TF4YEoikiPkbr2WPeFdXNeGvPMN2FvcgNPVGtyURkMajqBw9kP2bjbaYbYhWR2EC07OV2YznFUy55b+Hi1rQXpMMM5U9z280aI3o0Wv6fZauJy9GtmxSikemJKIhRPioAjkRvU7uUSICYkqTEhU4Zms4WhoM1JlPgdROPshR5ZyK50MOQCsLcMdFaPAxUHsKVinMUAuDnCocl9Dm9Gtwxp/vDUVc0ZHo6q5c9igurUDtRoDrk8J94rhAjZ/eHkrCmc/dLqq1e5jTWYrksJkaGgzOrzaUMDnYXikHEazFQazDXqTBTqT1alhEntIRQKkRQUPuhpcY7sJ4xNCcLy81TUNG6T510XjkelDwOPxEBMixSS2G0Q8gsLZD8UqA+3enUMk5KO0SgcACBILEC6XIFgSALGQD4AHo9kKrcGCxjZjt56mSibEhfq2HnU4AEAk4CFIHACpSABxgADiAD5EAfzOFXs/r9oDOsucWhnAarOhw2xDWaMOxj4Krw+PkENrMLmsTOfx8laMjlV0W5RztXC5GGFBIpy/3O62HzYAMDIqGDl3jKa5wX6IwtkPpcUE48DFxgGPkwoFOHvV2Gu70Yp2o67P44NEAoTJxQiWCqHtMPf5A8BkZTofoDn4EC2A3xnCikAhdEYLShp0sNhsGBuvdLjqnj2qmjsQLAmA3mRFYpgMKpkIZosN1a0dqG8zoqHNiLAgEYaGB+FCvetqeYQFiTAjJRwzh6sxc3g46wt6CDsonP3QqBiFXcelxQTjmAMLM9pNVrS7cb88iw0ovmosmc8DJiWpcKjE9cEMAM16E9RyMTrM1j5nfjS2m9DY3owAPpAUFgSrjXG4+BGfB1wXF9IVxunRCqemvxHfQuHsh+wN50YOLqq4mo0BrG5eMW3vbBOLDThe3gI+D5iQoERFs37Az6ZGyvHQtCTcODJiUA9eiW+icPZD8apAyCUBaOunvoJQwIPOyN3lyFxlY4Bj5S0YHhHUZzhPTw7DI9OHYHpyGI0lkz5ROPshHo+H9GgFDpU09XmM2cogUiFBQzu3e89cVXy5HWnRwfjp59oeQgEP88bE4OHpSRgRRRsQk4FROPsJg9mKo2XN+PFiE3682IgzNb3PQrja6WoNp6aUeRuz1YZgSQDunZSAB6cmIlLhnxuVEudQOPsoi9WG09UaHLzUhAMXGnG8oqVHsX17nL/cDpVMhGZdzylxbJmYqESL3gyjxYpzdhYqcrVAIR+xqkAES4Tg83kQ8Hn405wREF6z1VKUQgqZmL7NiOPoX40PsdkYbD5aib3FnTuf9DembK82gwWjYxWcCGeZSIBh6iAcKWvB6FiFw8vKZSIBZOIAh5aUiwN4iFUGIiRQiAA+H3qTFfVtBlzWGnH+8i/Xf2VeGkZE2/eglRB7UDj7ED6fh+viQvDRoTKXBPMVp6o0Du2+7Q5xSikYACd/XhQiCRh47m+MUorIYAn4PKBea0R5sx46kxXp0cHg83k4Xa1BX/XxMxKVqG7pQK3WgEsNfc/tBjrLct4zMd7RWyKkXxTOPmZkdDC2L52Gt3MvYP2+S3DV4rWL9e1QBQpZqb52XVwIii+3oeOqUp71bd3nEkuFAiSFBUIuEaLDbEVZkw7VLR2obulZuvTMzw/pIuRiJITJUFzX1q3+9MQklUOLWp69KZV2jiYux2MYL9lbZwBarRYKhQIajQbBwfQ0HACOlTXjmc9OotxFC0OurWPsbjwAGf0E5YQEJfg8HhrajShr1A24sWtfhAIeRseGoFVvgjiAj6K6Nrt/qI2JC8EXT06lKXHEbvZmFYWzj9MZLcj55hw+PlzhkvONjQvBicpWl5yrPwppAGJCAu3emcRVhkcGQcDj233dzY9OxuQhoW5uFfEl9mYV/S7m42TiALw6fxQ+fGgiIoIHX7axtEmHEDfXDB4aLoNEKPB4MANAcV07ztZqkRopR7I6qN9jZ49QUzATt6Gesx/R6M34044z2F5YY/dnVDIRhobLur0mEwVAYzDDbLHBYLFCZ7Si3WBGmwtWFI5PUOJ0tcapaX+uFhYkgtZg6bUtcnEAvl8xA1EK7mxNRbyDvVlFDwT9iCJQiHcXjUXWyEi88W0Rqls7Bix3GS4X270rNY8HBEuECBIHIFAkgEQogCiABwGfDx4ABoDVaoPJaoPBbEOH2QqtwQxthwUBfGBsvGM7YLtbY7sJExNVve4c8/ycERTMxK0onP3QnNFRmDM6CharDfVtRtS0du6sUdNqQK2m4+c/G1Dj4CatDNO567YzO28HCPicCuYrLja0QxzA71ZHenpyGO7OiGOxVcQfUDj7sQABH9EhUkSHSDGhj2Muawy44e97B9yRerAMZvaHMXrTrDN1m1onEwmQc8comp1B3I4eCJJ+RSgkWDIlke1msOri5bauDWNX3joCscpAlltE/AGFMxnQI9OTEOjHu3E0683QGc2YOzoK99FKQOIhFM5kQKFBYr9fnmy0MKhq7aAdSojHUDgTuxTVsVP9jUtaWVi6TvwXhTMZ0PHyFvx4se/C/P6iVc9+ZT7iPyicyYD+sfsC203gBE2HGTZXVZIiZAAOhXNOTg4yMjIgl8uhVqsxf/58FBcXdzvm5ZdfRmpqKmQyGZRKJWbPno38/PwBz/35559j5MiREIvFGDlyJLZt2+bYnRC3aGw3Ym9xA9vN4AQbA5eWYiWkPw6Fc15eHrKzs3H48GHk5ubCYrEgKysLOt0v9W5TUlKwZs0anD59GgcOHEBiYiKysrLQ0ND3N/ihQ4dw9913Y/HixTh58iQWL16MhQsX2hXqxL3CgsSYnhzGdjM4o4WGNoiHDKq2RkNDA9RqNfLy8jBjxoxej7myjnzXrl2YNWtWr8fcfffd0Gq1+Oabb7peu/nmm6FUKvHpp5/a1RaqreE+R0qbsfBfh9huBid8kT0N18WFsN0M4sU8UpVOo+ms7atSqXp932QyYf369VAoFBgzZkyf5zl06BCysrK6vXbTTTfh4MGDfX7GaDRCq9V2+yLuMTFJhUlJvf8d+xvqORNPcTqcGYbBihUrkJmZifT09G7vffXVVwgKCoJEIsHbb7+N3NxchIX1/atxXV0dIiIiur0WERGBurq6Pj+Tk5MDhULR9RUXR7UO3Gl0LO2PB3RW9iPEE5wO56VLl+LUqVO9DjvccMMNKCwsxMGDB3HzzTdj4cKFqK+v7/d819YqYBim3/oFq1atgkaj6fqqrKx07kbIgFp0Jnx6hP77AtRzJp7jVDgvW7YMO3bswJ49exAbG9vjfZlMhmHDhmHy5MnYsGEDAgICsGHDhj7PFxkZ2aOXXF9f36M3fTWxWIzg4OBuX8Q9NhwoRbuRZikAtBCFeI5D4cwwDJYuXYqtW7di9+7dSEpKsvtzRmPf29FPmTIFubm53V77/vvvMXXqVEeaR9xAozfjg4NlbDeDMxzZ+JWQwXCoZGh2djY2bdqE7du3Qy6Xd/V2FQoFpFIpdDodXnvtNcybNw9RUVFoamrC2rVrUVVVhQULFnSdZ8mSJYiJiUFOTg4AYPny5ZgxYwbeeOMN3H777di+fTt27dqFAwcOuPBWiTPMNht0Juo1X3GopAlHSpsxkR6QEjdzqOe8bt06aDQazJw5E1FRUV1fW7ZsAQAIBAIUFRXhzjvvREpKCm677TY0NDRg//79SEtL6zpPRUUFamtru/48depUbN68GRs3bsTo0aPxwQcfYMuWLZg0aZKLbpM4KyxIjLE0daybd384z3YTiB+gPQTJgP655yJWf1c88IF+5LPHpyAjkXrPxHG0+zZxmdkj+n4w66/e3UX1Roh7UTiTAaVEBCFWSZuZXu3AxUYc62XjV0JchcKZDIjH4+GFOSMxJFzGdlM45d0fqPdM3IfCmdjl5vRI7Hr6evznwQmYOjSU7eZwwv4L1Hsm7kPhTOzG5/Pwq9QIbHpkMnb+LhN3jI1BgJ9v20S9Z+IuFM7EKWnRCrx193U48Nyv8OTMoRAH+Oc/pf0XGnG8nHrPxPX88zuKuEykQoLfzUpmuxmseodmbhA3oHAmg3a0rBlGi43tZrCGes/EHSicyaDtO0/bWFHvmbgahTMZtP0XGtluAus6e88tbDeD+BAKZzIo9VoDiura2G4GJ9DMDeJKFM5kUKjX/It95xuo90xchsKZDMr+CzTefDXqPRNXoXAmTrPZGBy4SD3nq+0734CCCuo9k8GjcCZOO1enRWM77al3LapYR1yBwpk4jcabe5dHvWfiAhTOxGk03tw36j2TwaJwJk7pMFlxtJR6h33JO9+An2o0bDeDeDEKZ+IUPh+4MY12SOnPhcvtbDeBeDEKZ+IUcYAA/1g0Fk/OHMp2Uziroc3IdhOIF6NwJk7j83n4w82p+OsdoyDw87rOvWlsp3AmzqNwJoO2aGI8PvhNBuTiALabwinUcyaDQeFMXGJ6cjg2PTKZ7Wa4TWqkHNfFhWB8ghISOzcWaKCeMxkE6uoQlxkVq8DERBWO+NC+euFBYsSqpDhR0dr1WqBIgAkJSmgM5n4f+lHPmQwG9ZyJS901IZbtJriEkM/DpCQV2ozmbsEMAHqTFcfKW3DhcjviVFJMTFJCGSjscQ5aPUkGg8KZuNScUVEIFAnYbsagjI5VIEwuRn5pMwzm/nd4qWzuwJHSFrQZLBgTq8B1cSGQ/Xz/zTojrDbGE00mPoiGNYhLycQBmDMqCp8dr2K7KQ6LDZEiRCbEqSrHF49YbAxO/vw5AZ+H1Eg5FFIhGtsMiFBIXd1U4geo50xcbsGEOLab4LAgsQB1WgPOVGsHfS6rjUFRXRvyS5tR6ETQEwJQOBM3yEhUIjE0kO1mOCRcLoHFDUMQhy41ufycxD9QOBOX4/F4uGu8dz0YDJa4Z4SPwpk4i8KZuMUd42LB86JFg+IA9zzELL7chqoWvVvOTXwbhTNxi+gQKaYnh7PdDE549KPjaDda2G4G8TIUzsRtFnjR0IbRYnXbuc/WarFsUwEs1v6n5RFyNQpn4jY3joxw21iuq2kN7u3Z7iluwEs7fgLD0LxnYh8KZ+I2EqEAt18Xw3Yz7OKJpdaf5Fdg/b4St1+H+Abv6NYQr/WbaYlgwKCkQYeSBh3qtAa2m9SDXCxAm4fGhHO+KUKMUorbRkd75HrEe1E4E7caEh6EV+eP6vqzzmhBaaMOlxraOwO7UYeShnaUNuqgN7lv3Lc/YXIJ2ow6j11vxX9PIjJYggmJKo9dk3gfCmfiUTJxANJjFEiPUXR7nWEY1GkNP/ew23HpquCubu2AO4dqPT0ubrLY8MhHx7D1yWlICpN59NrEe1A4E07g8XiIUkgRpZBi2rCwbu8ZzFaUNnYOi7z7w3mcd/HefO6a49yfFr0Zv9l4BFufnAaVTOTx6xPuc+iBYE5ODjIyMiCXy6FWqzF//nwUFxd3vW82m/Hcc89h1KhRkMlkiI6OxpIlS1BTUzPgud955x0MHz4cUqkUcXFxePrpp2EwcG98knieRChASoQcR8uaXR7MbCpr0uPhD4/CYGZnOIdwm0PhnJeXh+zsbBw+fBi5ubmwWCzIysqCTtc5XqfX61FQUIAXX3wRBQUF2Lp1K86fP4958+b1e95PPvkEK1euxEsvvYRz585hw4YN2LJlC1atWuX8nRGfoTNa8OhHx/DBwTK3nN+dc5wHUlDRimf+exI2Ki1KrsFjBjHxsqGhAWq1Gnl5eZgxY0avxxw9ehQTJ05EeXk54uPjez1m6dKlOHfuHH744Yeu15555hkcOXIE+/fvt6stWq0WCoUCGo0GwcHBjt8M4aTLWgMe+uAofqoZfLW4viSFyVDa6LkHgr15bMYQrLp1BKttIJ5hb1YNap6zRtNZDlGl6vups0ajAY/HQ0hISJ/HZGZm4vjx4zhy5AgAoKSkBF9//TXmzJnT52eMRiO0Wm23L+JbztVqMf+fP7o1mAFubCf1r30l+PRIBdvNIBzi9ANBhmGwYsUKZGZmIj09vddjDAYDVq5ciXvvvbffnxCLFi1CQ0MDMjMzwTAMLBYLnnjiCaxcubLPz+Tk5OCVV15xtvmE4/YW1yP7kwLo3Dy9zpNznAfyypc/YcqQUCTSDA6CQfScly5dilOnTuHTTz/t9X2z2YxFixbBZrNh7dq1/Z5r7969eO2117B27dquseqvvvoKf/nLX/r8zKpVq6DRaLq+Kisrnb0VwjGfHavEbz885vZgBjrnOHOFwWzDs/+j8WfSyame87Jly7Bjxw7s27cPsbE9i9uYzWYsXLgQpaWl2L1794BjwC+++CIWL16Mhx9+GAAwatQo6HQ6PProo3j++efB5/f8GSIWiyEWi51pPuEwm43B6u+KPbb3Htdqfxwta8HGg2X4bWYS200hLHOo58wwDJYuXYqtW7di9+7dSErq+Q/oSjBfuHABu3btQmho6IDn1ev1PQJYIBCAYRgqFONnztRoUO/BMWA25jgP5G/fFqGkwXemDBLnOBTO2dnZ+Pjjj7Fp0ybI5XLU1dWhrq4OHR0dAACLxYK77roLx44dwyeffAKr1dp1jMn0yzbxS5Ys6TZNbu7cuVi3bh02b96M0tJS5Obm4sUXX8S8efMgEHDvm4e4z65z9Z69IAc3BDBabHj2f6do524/59DvdOvWrQMAzJw5s9vrGzduxIMPPoiqqirs2LEDAHDdddd1O2bPnj1dn6uoqOjWU37hhRfA4/HwwgsvoLq6GuHh4Zg7dy5ee+01B2+HeLsfzl326PUuXm7D8Eg5iuvaPHrdgRwvb8F/DpTikRlD2G4KYcmg5jlzCc1z9g17iurx6s6zuNTguXnHAh4wLkGJo2UtHrumPUQBfHz9u+kYpg5iuynEhTwyz5kQV7shVY1vn5qBV+alISRQ6JFrWpnOB3Fj40I8cj17mSw2/P6zkzS84aconAnnCAV8PDA1EXm/vwEPTUtCAN+9A8N8HjAxUYUTla1uvY4zypt0+KlGw3YzCAtoWINw3qWGduR8fc4tDwtDAoWIUkhwrpZbY85RCgkemT4EiybGIVDErel+ZHDszSoKZ+I19l9owKtfnUPxZdcE6dBwGdoMFo9O3RtIUpgMT1w/FPPHxkAUQL/Y+iJ7s4p+JBOvMT05HDt/F4otxyrx1vfn0aQzDfyhPoyPV+J0jQYmCzd2xB4ZFYzsG4bh5vRICNw8jEO8A4Uz8SoBAj7um5SAuWOi8c/dF/GfH0thttr/yx+fB0xIUOFIWbMbW9k/Hg+IVkgxJFyGIWEy3JCqxvUp4eDxKJTJL2hYg3i18iYdcr4uwrc/1dl1/HVxChRWeuYBmzJQiKQwGZLCgrqCeEh4EBJCAyER0uIqf0XDGsQvJITK8N7i8ThdpUFJYzuadSa06Exo0pnQojehqd2EExWtMFltmJSkQn6pe3rMwyPkmDVCjSHhQUgK6wxiJW0/RQaBwpn4hFGxCoyKVfT63tGyZryVW4xDl9w3lHGxoR03pKpx2+go6hUTl6DHwcTnZSSq8OaC6zAhQem2a1htDN7Lu4Sb39mHgxcb3XYd4j8onIlfiAmRYvOjk/G7Wclw52SIsiY97v13Pv7wv5PQ6M3uuxDxeRTOxG8ECPhYcWMKNj0yGZHB7i2y/99jVZj1Vh52nqrttextY7txwHK4BrMVR0qbsW7vJZx181ZdhHtotgbxSy06E5793yns8kAVvDvHxeLNBaO7pspZbQzG/SUXQgEP4+KVGJegxLh4JSKDJSisakVBeQsKKlpwtkYLi41BnEqK3c/MhFBAfSlfQLM1COmHUibC+0vG46ND5Xjt63NuXYzyeUEVfpWqxpzRUQA6N67VdHQOeXx/9jK+P9v/D4hlNyRTMPsh+hsnfovH4+GBqYn44slpGBru3k1VX9rxU9cY9MFL9j8wjFNJ8etxMe5qFuEwCmfi90ZGB+PLZZlYlBHntms0thvx9H8L0aIz4eClJrs/R71m/0VjzoRc5cuTNfjj1tNoM1rccv5QmQirbk2FjQHyihuw83Rtn8fSWLNvomL7hDhh7phofL18OsbGh7jl/E06E24cEYmFE+IwJq73RTNXUK/Zv9HfPCHXiFMF4r+PTcGTM4fCHbWIKpr1KGvUwdLPDic01kxotgYhvRAK+PjDzamYNiwMT20pRIMLaz7PXXMAABAsCcBnj01BndaA/NIm5Jc040J9OwDqNRMacyZkQE3tRvz+s5PYU9zg8nMPCZNh25PToPh5v8SmdiOOlrVg1gg1hbOPojFnQlwkNEiM/zyYgRdvGwmhwLXjHCWNOiz9tAAWq63rWjenR1IwEwpnQuzB4/Hw28wkbHtyGpLCXDsnev+FRry685xLz0m8H4UzIQ5Ij1Hgy2WZuHNcrEvP+8HBMnySX97vMWarDZuPVOB4OXu7uBDPoXAmxEFB4gD8feEY3Dsp3qXnfWn7T72uHjRbbdhytAI3vLkXK7eeRt55KknqDyicCXGSXOzayU4WG4MnPylAWaOu67X6NgNufXc/nvv8NKpaOgAAhZWtLr0u4SYKZ0Kc1GG2uvycrXozHv7oGLSGzjocarkEHzw0EXeOi+2ac32yshW2fuZIE99A4UyIkzpMrg9nALhY345lm07A+nMAx4RI8feFY/DVskxMTw6DpsOM0ibdAGch3o7CmRAn6d3Qc74i73wDXv+6+wyOtGgF/u+3k/DRQxO7So4S30UrBAlxlptHFjYcKEVKRBDuzuj+4HFGSrh7L0w4gXrOhDjpiZlDIXDnhoQAXvjiDPJL7C8xSnwHhTMhTkqPUeDh6UluvYbZyuCJTwpQ2ax363UI91A4EzIIT89OQWJooFuv0awz4ZGPjrn1GoR7KJwJGQSJUICcO0a7/TqXtQa3X4NwC4UzIYM0ZWgo7pno2tWC11LKRG49P+EeCmdCXGDVramICBa77fyhFM5+h8KZEBcIlgjx6vxRbju/MpDC2d9QOBPiIjeOjMBjM4a4ZWur0CAKZ3/jUDjn5OQgIyMDcrkcarUa8+fPR3Fxcdf7ZrMZzz33HEaNGgWZTIbo6GgsWbIENTU1A567tbUV2dnZiIqKgkQiwYgRI/D11187fkeEsGjVrSPw5dJMTEhQuvS8CimFs79xKJzz8vKQnZ2Nw4cPIzc3FxaLBVlZWdDpOtf56/V6FBQU4MUXX0RBQQG2bt2K8+fPY968ef2e12Qy4cYbb0RZWRn+97//obi4GO+//z5iYmiDS+J90mMU+OzxKXj77jFQy10zDm1w41Jxwk2D2kOwoaEBarUaeXl5mDFjRq/HHD16FBMnTkR5eTni43t/ov3ee+9h9erVKCoqglAodKottIcg4aJ2owX/2H0B/zlQCrPV+fXec8dE4x/3jHVhywhb7M2qQdXW0Gg0AACVStXvMTweDyEhIX0es2PHDkyZMgXZ2dnYvn07wsPDce+99+K5556DQCDo9TNGoxFG4y87Imu1WudughA3ChIHYNUtI7BkSiIKyltwrlaLoro2nKvVolZj/9zlFp3Jja0kXOR0ODMMgxUrViAzMxPp6em9HmMwGLBy5Urce++9/f6EKCkpwe7du3Hffffh66+/xoULF5CdnQ2LxYI//elPvX4mJycHr7zyirPNJ8SjYkKkiAmRYu6Y6K7XWnQmnKvV4p0fLuBIaf9bTzVROPsdp4c1srOzsXPnThw4cACxsT33UzObzViwYAEqKiqwd+/efsM5JSUFBoMBpaWlXT3lt956C6tXr0ZtbW2vn+mt5xwXF0fDGsTrWG0M/rXvEt76/jwsfRTRjwyW4PAfZ3m4ZcQd3DqssWzZMuzYsQP79u3rM5gXLlyI0tJS7N69e8CwjIqKglAo7DaEMWLECNTV1cFkMkEk6vmkWiwWQyx236R/QjxFwOfhyZnDkDksDE9tLkRJY89C+s06ExiGAc8d8/QIJzk0W4NhGCxduhRbt27F7t27kZTUsyLXlWC+cOECdu3ahdDQ0AHPO23aNFy8eBE2m63rtfPnzyMqKqrXYCbEF42ODcFXv8vsdSm4yWqDzk07rxBuciics7Oz8fHHH2PTpk2Qy+Woq6tDXV0dOjo6N560WCy46667cOzYMXzyySewWq1dx5hMv4yZLVmyBKtWrer68xNPPIGmpiYsX74c58+fx86dO/H6668jOzvbRbdJiHcIFAUg545RWL94PJSB3WcuNbfTuLM/cWjMua9fqTZu3IgHH3wQZWVlvfamAWDPnj2YOXMmAGDmzJlITEzEBx980PX+oUOH8PTTT6OwsBAxMTH47W9/2+9sjWvRVDria+q1Buw4WQNloAgqmQgTk1SQuXjHb+J59mbVoOY5cwmFMyHEG9ibVVRbgxBCOIjCmRBCOIjCmRBCOIjCmRBCOIjCmRBCOIjCmRBCOIjCmRBCOIjCmRBCOIjCmRBCOIjCmRBCOIjCmRBCOIjCmRBCOIjCmRBCOIjCmRBCOMhnisNeqXxKu3ATQrjsSkYNVK3ZZ8K5ra0NABAXF8dySwghZGBtbW1QKBR9vu8zxfZtNhtqamogl8vdsgnmld29KysrvbqYP90Hd/jCPQC+cR+evAeGYdDW1obo6Gjw+X2PLPtMz5nP5/e6E7irBQcHe+0/wKvRfXCHL9wD4Bv34al76K/HfAU9ECSEEA6icCaEEA6icLaTWCzGSy+9BLFYzHZTBoXugzt84R4A37gPLt6DzzwQJIQQX0I9Z0II4SAKZ0II4SAKZ0II4SAKZ0II4SC/COd9+/Zh7ty5iI6OBo/HwxdffNHt/a1bt+Kmm25CWFgYeDweCgsLe5zjsccew9ChQyGVShEeHo7bb78dRUVFA1577dq1SEpKgkQiwfjx47F//36vu4+cnBxkZGRALpdDrVZj/vz5KC4u9qp7uPZ+eDwennrqKafuge37qK6uxv3334/Q0FAEBgbiuuuuw/Hjx73mHiwWC1544QUkJSVBKpViyJAh+POf/wybzebwPbjqPq5gGAa33HJLr+fpjSu/v6/lF+Gs0+kwZswYrFmzps/3p02bhr/+9a99nmP8+PHYuHEjzp07h++++w4MwyArKwtWq7XPz2zZsgVPPfUUnn/+eZw4cQLTp0/HLbfcgoqKCq+6j7y8PGRnZ+Pw4cPIzc2FxWJBVlYWdDqd19zDFUePHsX69esxevRoh9t+bTvZuI+WlhZMmzYNQqEQ33zzDc6ePYu///3vCAkJ8Zp7eOONN/Dee+9hzZo1OHfuHP72t79h9erV+Mc//uHwPbjqPq5455137C7/4Orv7x4YPwOA2bZtW6/vlZaWMgCYEydODHiekydPMgCYixcv9nnMxIkTmccff7zba6mpqczKlSsdaXKvPHkf16qvr2cAMHl5eXZ/pjeevoe2tjYmOTmZyc3NZa6//npm+fLljje6F568j+eee47JzMx0sqV98+Q9zJkzh3nooYe6vXbHHXcw999/vyNN7tVg7qOwsJCJjY1lamtr+z3PFe78/mYYhvGLnrOr6XQ6bNy4EUlJSX1WwTOZTDh+/DiysrK6vZ6VlYWDBw96opkDsuc+eqPRaAAAKpXKXU2zmyP3kJ2djTlz5mD27Nkeap397L2PHTt2YMKECViwYAHUajXGjh2L999/34Mt7Zu995CZmYkffvgB58+fBwCcPHkSBw4cwK233uqppvag1+txzz33YM2aNYiMjBzweE98f1M4O2Dt2rUICgpCUFAQvv32W+Tm5kIkEvV6bGNjI6xWKyIiIrq9HhERgbq6Ok80t0+O3Me1GIbBihUrkJmZifT0dDe3tG+O3sPmzZtx/Phx5OTkeLCVA3P0PkpKSrBu3TokJyfju+++w+OPP47f/e53+OijjzzY6u4cvYfnnnsO99xzD1JTUyEUCjF27Fg89dRTuOeeezzY6u6efvppTJ06Fbfffrtdx3vi+5vC2QH33XcfTpw4gby8PCQnJ2PhwoUwGAz9fuba8SuGYdxS0tQRztzHFUuXLsWpU6fw6aefurmV/XPkHiorK7F8+XJ88sknkEgkHm5p/xz9u7DZbBg3bhxef/11jB07Fo899hgeeeQRrFu3zoOt7s7Re9iyZQs+/vhjbNq0CQUFBfjwww/x5ptv4sMPP/Rgq3+xY8cO7N69G++8847Dn3Xn97fPlAz1BIVCAYVCgeTkZEyePBlKpRLbtm3r9Sd+WFgYBAJBj5+i9fX1PX7aepoj93G1ZcuWYceOHdi3b59HyrP2x5F7OH78OOrr6zF+/Piu16xWK/bt24c1a9bAaDRCIBB4svldHP27iIqKwsiRI7u9NmLECHz++eeeaG6vHL2HZ599FitXrsSiRYsAAKNGjUJ5eTlycnLwwAMPeLLpAIDdu3fj0qVLPR6q3nnnnZg+fTr27t3b4zOe+P6mnvMgMAwDo9HY63sikQjjx49Hbm5ut9dzc3MxdepUTzTPbv3dx5X3ly5diq1bt2L37t1ISkryYOvs0989zJo1C6dPn0ZhYWHX14QJE3DfffehsLCQtWDuzUB/F9OmTesxjfH8+fNISEhwd9PsNtA96PX6HkXmBQKB01PpBmvlypU4depUt38fAPD2229j48aNvX7GE9/fftFzbm9vx8WLF7v+XFpaisLCQqhUKsTHx6O5uRkVFRWoqakBgK5//JGRkYiMjERJSQm2bNmCrKwshIeHo7q6Gm+88QakUmm3hxizZs3Cr3/9ayxduhQAsGLFCixevBgTJkzAlClTsH79elRUVODxxx/3qvvIzs7Gpk2bsH37dsjl8q7egkKhgFQq5fw9yOXyHuPjMpkMoaGhTo+bs/V3cWVs9PXXX8fChQtx5MgRrF+/HuvXr/eae5g7dy5ee+01xMfHIy0tDSdOnMBbb72Fhx56yOF7cMV9XPm6Vnx8fLeOiLu/v3twyZwPjtuzZw8DoMfXAw88wDAMw2zcuLHX91966SWGYRimurqaueWWWxi1Ws0IhUImNjaWuffee5mioqJu10lISOj6zBX//Oc/mYSEBEYkEjHjxo0b1PQztu6jt3MCYDZu3Og193CtwU6lY/M+vvzySyY9PZ0Ri8VMamoqs379eq+6B61WyyxfvpyJj49nJBIJM2TIEOb5559njEYjK/fRG/Qylc7d39/XopKhhBDCQTTmTAghHEThTAghHEThTAghHEThTAghHEThTAghHEThTAghHEThTAghHEThTAghHEThTAghHEThTAghHEThTAghHEThTAghHPT/AZl5ze48Xkh1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gz.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31f95664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sz.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ceca403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VehicleNum</th>\n",
       "      <th>Time</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Lat</th>\n",
       "      <th>OpenStatus</th>\n",
       "      <th>Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34745</td>\n",
       "      <td>20:27:43</td>\n",
       "      <td>113.806847</td>\n",
       "      <td>22.623249</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27368</td>\n",
       "      <td>09:08:53</td>\n",
       "      <td>113.805893</td>\n",
       "      <td>22.624996</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22998</td>\n",
       "      <td>10:51:10</td>\n",
       "      <td>113.806931</td>\n",
       "      <td>22.624166</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22998</td>\n",
       "      <td>10:11:50</td>\n",
       "      <td>113.805946</td>\n",
       "      <td>22.625433</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22998</td>\n",
       "      <td>10:12:05</td>\n",
       "      <td>113.806381</td>\n",
       "      <td>22.623833</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22396</td>\n",
       "      <td>12:14:28</td>\n",
       "      <td>113.806602</td>\n",
       "      <td>22.623949</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32436</td>\n",
       "      <td>08:15:34</td>\n",
       "      <td>113.806465</td>\n",
       "      <td>22.624166</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>32436</td>\n",
       "      <td>08:15:04</td>\n",
       "      <td>113.807053</td>\n",
       "      <td>22.626350</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>32436</td>\n",
       "      <td>08:15:19</td>\n",
       "      <td>113.806252</td>\n",
       "      <td>22.625933</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>32436</td>\n",
       "      <td>08:14:49</td>\n",
       "      <td>113.807053</td>\n",
       "      <td>22.626333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>32436</td>\n",
       "      <td>18:10:57</td>\n",
       "      <td>113.806885</td>\n",
       "      <td>22.622967</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>32436</td>\n",
       "      <td>18:10:27</td>\n",
       "      <td>113.806450</td>\n",
       "      <td>22.626083</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>32436</td>\n",
       "      <td>18:35:15</td>\n",
       "      <td>113.806686</td>\n",
       "      <td>22.623632</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32436</td>\n",
       "      <td>18:10:42</td>\n",
       "      <td>113.805984</td>\n",
       "      <td>22.624683</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>27373</td>\n",
       "      <td>18:58:43</td>\n",
       "      <td>113.805969</td>\n",
       "      <td>22.624983</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>27373</td>\n",
       "      <td>08:42:34</td>\n",
       "      <td>113.807114</td>\n",
       "      <td>22.623211</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>31910</td>\n",
       "      <td>09:49:56</td>\n",
       "      <td>113.806801</td>\n",
       "      <td>22.626200</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>35161</td>\n",
       "      <td>00:43:41</td>\n",
       "      <td>113.807030</td>\n",
       "      <td>22.622833</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>35161</td>\n",
       "      <td>00:43:25</td>\n",
       "      <td>113.807098</td>\n",
       "      <td>22.624184</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>23411</td>\n",
       "      <td>20:20:06</td>\n",
       "      <td>113.806549</td>\n",
       "      <td>22.623550</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    VehicleNum      Time         Lng        Lat  OpenStatus  Speed\n",
       "0        34745  20:27:43  113.806847  22.623249           1     27\n",
       "1        27368  09:08:53  113.805893  22.624996           0     49\n",
       "2        22998  10:51:10  113.806931  22.624166           1     54\n",
       "3        22998  10:11:50  113.805946  22.625433           0     43\n",
       "4        22998  10:12:05  113.806381  22.623833           0     60\n",
       "5        22396  12:14:28  113.806602  22.623949           1     45\n",
       "6        32436  08:15:34  113.806465  22.624166           0     50\n",
       "7        32436  08:15:04  113.807053  22.626350           0      0\n",
       "8        32436  08:15:19  113.806252  22.625933           0     22\n",
       "9        32436  08:14:49  113.807053  22.626333           0      0\n",
       "10       32436  18:10:57  113.806885  22.622967           0     50\n",
       "11       32436  18:10:27  113.806450  22.626083           0     36\n",
       "12       32436  18:35:15  113.806686  22.623632           1     29\n",
       "13       32436  18:10:42  113.805984  22.624683           0     41\n",
       "14       27373  18:58:43  113.805969  22.624983           0     42\n",
       "15       27373  08:42:34  113.807114  22.623211           0     61\n",
       "16       31910  09:49:56  113.806801  22.626200           0     25\n",
       "17       35161  00:43:41  113.807030  22.622833           1     51\n",
       "18       35161  00:43:25  113.807098  22.624184           1     54\n",
       "19       23411  20:20:06  113.806549  22.623550           0     56"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Preprocessing\n",
    "# Delete the data outside of the study area\n",
    "data = tbd.clean_outofshape(data, sz, col=['Lng', 'Lat'], accuracy=500)  #剔除超出研究区域的数据\n",
    "data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a348659b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>114.313499</td>\n",
       "      <td>22.681601</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>543136</th>\n",
       "      <td>28265</td>\n",
       "      <td>17:35:02</td>\n",
       "      <td>114.313202</td>\n",
       "      <td>22.684299</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>543137</th>\n",
       "      <td>28265</td>\n",
       "      <td>21:14:40</td>\n",
       "      <td>114.332497</td>\n",
       "      <td>22.710400</td>\n",
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      "text/plain": [
       "        VehicleNum      Time         Lng        Lat  OpenStatus  Speed\n",
       "0            34745  20:27:43  113.806847  22.623249           1     27\n",
       "1            27368  09:08:53  113.805893  22.624996           0     49\n",
       "2            22998  10:51:10  113.806931  22.624166           1     54\n",
       "3            22998  10:11:50  113.805946  22.625433           0     43\n",
       "4            22998  10:12:05  113.806381  22.623833           0     60\n",
       "...            ...       ...         ...        ...         ...    ...\n",
       "543133       28265  17:27:30  114.298500  22.671801           0     33\n",
       "543134       28265  17:34:32  114.312698  22.683001           0     20\n",
       "543135       28265  17:34:02  114.313499  22.681601           0      3\n",
       "543136       28265  17:35:02  114.313202  22.684299           0     18\n",
       "543137       28265  21:14:40  114.332497  22.710400           0     45\n",
       "\n",
       "[543138 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f3cb1cd",
   "metadata": {},
   "outputs": [
    {
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       "      <td>22.693632</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>113.989250</td>\n",
       "      <td>22.693083</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
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       "      <td>22.691000</td>\n",
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       "      <td>00:07:41</td>\n",
       "      <td>113.988747</td>\n",
       "      <td>22.685450</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
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       "      <td>22.681749</td>\n",
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       "      <td>43</td>\n",
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       "      <td>113.986481</td>\n",
       "      <td>22.680332</td>\n",
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       "      <td>22.676416</td>\n",
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       "      <td>22.674232</td>\n",
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       "      <td>22.672600</td>\n",
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       "    <tr>\n",
       "      <th>446738</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:13:41</td>\n",
       "      <td>113.996902</td>\n",
       "      <td>22.669600</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
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       "      <th>429747</th>\n",
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       "      <td>00:14:21</td>\n",
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       "      <td>22.666767</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
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       "      <td>114.002869</td>\n",
       "      <td>22.661551</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
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       "      <td>22.661449</td>\n",
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       "      <td>22.660967</td>\n",
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       "      <td>22396</td>\n",
       "      <td>00:17:09</td>\n",
       "      <td>114.007202</td>\n",
       "      <td>22.660950</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
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      ],
      "text/plain": [
       "        VehicleNum      Time         Lng        Lat  OpenStatus  Speed\n",
       "452072       22396  00:00:29  113.996719  22.693333           1     20\n",
       "444077       22396  00:01:01  113.995514  22.695032           1     34\n",
       "444078       22396  00:01:09  113.995430  22.695766           1     41\n",
       "444075       22396  00:01:41  113.995369  22.696484           1      0\n",
       "444079       22396  00:02:21  113.995430  22.696650           1     17\n",
       "444073       22396  00:03:01  113.994934  22.697884           0     23\n",
       "444074       22396  00:03:41  113.992683  22.697350           0     18\n",
       "444076       22396  00:04:21  113.992348  22.696733           0     36\n",
       "452073       22396  00:05:01  113.992996  22.693632           0      2\n",
       "446704       22396  00:05:41  113.989250  22.693083           0     56\n",
       "422019       22396  00:06:21  113.986366  22.691000           0     48\n",
       "422218       22396  00:07:41  113.988747  22.685450           0     16\n",
       "426967       22396  00:08:21  113.989586  22.681749           0     43\n",
       "443922       22396  00:09:01  113.986015  22.681583           0      0\n",
       "443923       22396  00:09:41  113.986481  22.680332           0     47\n",
       "422292       22396  00:10:21  113.988297  22.676416           0     31\n",
       "422295       22396  00:11:01  113.991600  22.677517           0     41\n",
       "422291       22396  00:11:09  113.992149  22.677317           0     33\n",
       "443991       22396  00:11:41  113.993401  22.674368           0     31\n",
       "443987       22396  00:11:49  113.993446  22.674217           0      0\n",
       "443988       22396  00:12:21  113.993446  22.674232           0      0\n",
       "443993       22396  00:13:01  113.994049  22.672600           0     38\n",
       "446738       22396  00:13:41  113.996902  22.669600           0     19\n",
       "429747       22396  00:14:21  114.000130  22.666767           0     48\n",
       "429744       22396  00:14:29  114.000801  22.666166           0     46\n",
       "453228       22396  00:15:01  114.002518  22.662399           0     63\n",
       "430986       22396  00:15:09  114.002869  22.661551           0     28\n",
       "430987       22396  00:16:21  114.002968  22.661449           0      0\n",
       "430994       22396  00:17:01  114.006233  22.660967           0     55\n",
       "389726       22396  00:17:09  114.007202  22.660950           0     31"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete the data with instantaneous changes in passenger status\n",
    "data = tbd.clean_taxi_status(data, col=['VehicleNum', 'Time', 'OpenStatus'])\n",
    "#删除出租车数据中载客状态瞬间变化的记录，这些记录的存在会影响出行订单判断。\n",
    "#判断条件为:如果对同一辆车，上一条记录与下一条记录的载客状态都与本条记录不同，则本条记录应该删去\n",
    "#实际上就是按照编号和时间顺序将数据排好\n",
    "data.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e238ffc8",
   "metadata": {},
   "outputs": [
    {
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       "        VehicleNum      Time         Lng        Lat  OpenStatus  Speed\n",
       "452072       22396  00:00:29  113.996719  22.693333           1     20\n",
       "444077       22396  00:01:01  113.995514  22.695032           1     34\n",
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       "...            ...       ...         ...        ...         ...    ...\n",
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       "64391        36805  23:53:36  114.120354  22.544300           0      0\n",
       "64396        36805  23:53:51  114.120354  22.544300           0      0\n",
       "\n",
       "[542224 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    }
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   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2cfbce33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'slon': 113.6,\n",
       " 'slat': 22.4,\n",
       " 'deltalon': 0.004872390756896538,\n",
       " 'deltalat': 0.004496605206422906,\n",
       " 'theta': 0,\n",
       " 'method': 'rect',\n",
       " 'gridsize': 500}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data gridding\n",
    "# Define the bounds and generate gridding parameters\n",
    "bounds = [113.6, 22.4, 114.8, 22.9]\n",
    "params = tbd.area_to_params(bounds, accuracy=500)\n",
    "params\n",
    "#(113.6, 22.4, 0.004872390756896538, 0.004496605206422906)\n",
    "#栅格参数(lonStart,latStart,deltaLon,deltaLat)，分别为栅格左下角坐标与单个栅格的经纬度长宽"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1043ebd9",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        VehicleNum      Time         Lng        Lat  OpenStatus  Speed  \\\n",
       "452072       22396  00:00:29  113.996719  22.693333           1     20   \n",
       "444077       22396  00:01:01  113.995514  22.695032           1     34   \n",
       "444078       22396  00:01:09  113.995430  22.695766           1     41   \n",
       "444075       22396  00:01:41  113.995369  22.696484           1      0   \n",
       "444079       22396  00:02:21  113.995430  22.696650           1     17   \n",
       "\n",
       "        LONCOL  LATCOL  \n",
       "452072      81      65  \n",
       "444077      81      66  \n",
       "444078      81      66  \n",
       "444075      81      66  \n",
       "444079      81      66  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mapping GPS data to grids\n",
    "data['LONCOL'], data['LATCOL'] = tbd.GPS_to_grid(data['Lng'], data['Lat'], params)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "596dc88a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>00:07:41</td>\n",
       "      <td>113.988747</td>\n",
       "      <td>22.685450</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>80</td>\n",
       "      <td>63</td>\n",
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       "      <td>00:08:21</td>\n",
       "      <td>113.989586</td>\n",
       "      <td>22.681749</td>\n",
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       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>79</td>\n",
       "      <td>62</td>\n",
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       "      <th>422292</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:10:21</td>\n",
       "      <td>113.988297</td>\n",
       "      <td>22.676416</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>80</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422295</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:11:01</td>\n",
       "      <td>113.991600</td>\n",
       "      <td>22.677517</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>80</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422291</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:11:09</td>\n",
       "      <td>113.992149</td>\n",
       "      <td>22.677317</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>80</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443991</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:11:41</td>\n",
       "      <td>113.993401</td>\n",
       "      <td>22.674368</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>81</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443987</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:11:49</td>\n",
       "      <td>113.993446</td>\n",
       "      <td>22.674217</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        VehicleNum      Time         Lng        Lat  OpenStatus  Speed  \\\n",
       "452072       22396  00:00:29  113.996719  22.693333           1     20   \n",
       "444077       22396  00:01:01  113.995514  22.695032           1     34   \n",
       "444078       22396  00:01:09  113.995430  22.695766           1     41   \n",
       "444075       22396  00:01:41  113.995369  22.696484           1      0   \n",
       "444079       22396  00:02:21  113.995430  22.696650           1     17   \n",
       "444073       22396  00:03:01  113.994934  22.697884           0     23   \n",
       "444074       22396  00:03:41  113.992683  22.697350           0     18   \n",
       "444076       22396  00:04:21  113.992348  22.696733           0     36   \n",
       "452073       22396  00:05:01  113.992996  22.693632           0      2   \n",
       "446704       22396  00:05:41  113.989250  22.693083           0     56   \n",
       "422019       22396  00:06:21  113.986366  22.691000           0     48   \n",
       "422218       22396  00:07:41  113.988747  22.685450           0     16   \n",
       "426967       22396  00:08:21  113.989586  22.681749           0     43   \n",
       "443922       22396  00:09:01  113.986015  22.681583           0      0   \n",
       "443923       22396  00:09:41  113.986481  22.680332           0     47   \n",
       "422292       22396  00:10:21  113.988297  22.676416           0     31   \n",
       "422295       22396  00:11:01  113.991600  22.677517           0     41   \n",
       "422291       22396  00:11:09  113.992149  22.677317           0     33   \n",
       "443991       22396  00:11:41  113.993401  22.674368           0     31   \n",
       "443987       22396  00:11:49  113.993446  22.674217           0      0   \n",
       "\n",
       "        LONCOL  LATCOL  \n",
       "452072      81      65  \n",
       "444077      81      66  \n",
       "444078      81      66  \n",
       "444075      81      66  \n",
       "444079      81      66  \n",
       "444073      81      66  \n",
       "444074      81      66  \n",
       "444076      81      66  \n",
       "452073      81      65  \n",
       "446704      80      65  \n",
       "422019      79      65  \n",
       "422218      80      63  \n",
       "426967      80      63  \n",
       "443922      79      63  \n",
       "443923      79      62  \n",
       "422292      80      61  \n",
       "422295      80      62  \n",
       "422291      80      62  \n",
       "443991      81      61  \n",
       "443987      81      61  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "82d2173d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LONCOL</th>\n",
       "      <th>LATCOL</th>\n",
       "      <th>VehicleNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>36</td>\n",
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       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>67</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LONCOL  LATCOL  VehicleNum\n",
       "0      36      63           3\n",
       "1      36      64           2\n",
       "2      36      65           1\n",
       "3      36      66           1\n",
       "4      36      67           8"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregate data into grids\n",
    "#python中groupby函数主要的作用是进行数据的分组以及分组后地组内运算！\n",
    "datatest = data.groupby(['LONCOL', 'LATCOL'])['VehicleNum'].count().reset_index()\n",
    "datatest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f3b9ca25",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>LONCOL</th>\n",
       "      <th>LATCOL</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>3377</th>\n",
       "      <td>182</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3378</th>\n",
       "      <td>183</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3379</th>\n",
       "      <td>184</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3380</th>\n",
       "      <td>185</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3381</th>\n",
       "      <td>186</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3382 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      LONCOL  LATCOL  VehicleNum\n",
       "0         36      63           3\n",
       "1         36      64           2\n",
       "2         36      65           1\n",
       "3         36      66           1\n",
       "4         36      67           8\n",
       "...      ...     ...         ...\n",
       "3377     182      44           1\n",
       "3378     183      44           2\n",
       "3379     184      44           1\n",
       "3380     185      44           2\n",
       "3381     186      43           5\n",
       "\n",
       "[3382 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datatest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0325166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LONCOL</th>\n",
       "      <th>LATCOL</th>\n",
       "      <th>VehicleNum</th>\n",
       "      <th>geometry</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>63</td>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((113.77297 22.68104, 113.77784 22.681...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>64</td>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((113.77297 22.68553, 113.77784 22.685...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((113.77297 22.69003, 113.77784 22.690...</td>\n",
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       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((113.77297 22.69453, 113.77784 22.694...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>67</td>\n",
       "      <td>8</td>\n",
       "      <td>POLYGON ((113.77297 22.69902, 113.77784 22.699...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LONCOL  LATCOL  VehicleNum  \\\n",
       "0      36      63           3   \n",
       "1      36      64           2   \n",
       "2      36      65           1   \n",
       "3      36      66           1   \n",
       "4      36      67           8   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((113.77297 22.68104, 113.77784 22.681...  \n",
       "1  POLYGON ((113.77297 22.68553, 113.77784 22.685...  \n",
       "2  POLYGON ((113.77297 22.69003, 113.77784 22.690...  \n",
       "3  POLYGON ((113.77297 22.69453, 113.77784 22.694...  \n",
       "4  POLYGON ((113.77297 22.69902, 113.77784 22.699...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the geometry for grids\n",
    "datatest['geometry'] = tbd.grid_to_polygon([datatest['LONCOL'], datatest['LATCOL']], params)\n",
    "\n",
    "# Change it into GeoDataFrame\n",
    "# import geopandas as gpd\n",
    "datatest = gpd.GeoDataFrame(datatest)\n",
    "datatest.head()\n",
    "#计算出每个栅格里的出租车数量（不按时间，是一天数据中所有的点）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e8f28dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 4800x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the grids\n",
    "fig = plt.figure(1, (16, 6), dpi=300)\n",
    "ax1 = plt.subplot(111)\n",
    "#ax用于调整大小 \n",
    "# tbd.plot_map(plt, bounds, zoom=10, style=4)\n",
    "datatest.plot(ax=ax1, column='VehicleNum', legend=True)\n",
    "plt.xticks([], fontsize=10)\n",
    "plt.yticks([], fontsize=10)\n",
    "plt.title('Taxi GPS track point count', fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6f435e78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4800x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the grids\n",
    "fig = plt.figure(1, (16, 6), dpi=300) # 确定图形高为6，宽为8；图形清晰度\n",
    "ax1 = plt.subplot(111)\n",
    "datatest.plot(ax=ax1, column='VehicleNum', legend=True, scheme='quantiles')\n",
    "# plt.legend(fontsize=10)\n",
    "plt.xticks([], fontsize=10)\n",
    "plt.yticks([], fontsize=10)\n",
    "plt.title('Taxi GPS track point count', fontsize=12);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5f40c5f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the grids\n",
    "fig = plt.figure(1, (16, 6), dpi=150) # 确定图形高为6，宽为8；图形清晰度\n",
    "ax1 = plt.subplot(111)\n",
    "datatest.plot(ax=ax1, column='VehicleNum', legend=True, cmap='OrRd', scheme='quantiles')\n",
    "# plt.legend(fontsize=10)\n",
    "plt.xticks([], fontsize=10)\n",
    "plt.yticks([], fontsize=10)\n",
    "plt.title('Taxi GPS track point count', fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f65bc2c0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VehicleNum</th>\n",
       "      <th>stime</th>\n",
       "      <th>slon</th>\n",
       "      <th>slat</th>\n",
       "      <th>etime</th>\n",
       "      <th>elon</th>\n",
       "      <th>elat</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>427075</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:19:41</td>\n",
       "      <td>114.013016</td>\n",
       "      <td>22.664818</td>\n",
       "      <td>00:23:01</td>\n",
       "      <td>114.021400</td>\n",
       "      <td>22.663918</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131301</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:41:51</td>\n",
       "      <td>114.021767</td>\n",
       "      <td>22.640200</td>\n",
       "      <td>00:43:44</td>\n",
       "      <td>114.026070</td>\n",
       "      <td>22.640266</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417417</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:45:44</td>\n",
       "      <td>114.028099</td>\n",
       "      <td>22.645082</td>\n",
       "      <td>00:47:44</td>\n",
       "      <td>114.030380</td>\n",
       "      <td>22.650017</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376160</th>\n",
       "      <td>22396</td>\n",
       "      <td>01:08:26</td>\n",
       "      <td>114.034897</td>\n",
       "      <td>22.616301</td>\n",
       "      <td>01:16:34</td>\n",
       "      <td>114.035614</td>\n",
       "      <td>22.646717</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21768</th>\n",
       "      <td>22396</td>\n",
       "      <td>01:26:06</td>\n",
       "      <td>114.046021</td>\n",
       "      <td>22.641251</td>\n",
       "      <td>01:34:48</td>\n",
       "      <td>114.066048</td>\n",
       "      <td>22.636183</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175519</th>\n",
       "      <td>36805</td>\n",
       "      <td>22:49:12</td>\n",
       "      <td>114.114365</td>\n",
       "      <td>22.550632</td>\n",
       "      <td>22:50:40</td>\n",
       "      <td>114.115501</td>\n",
       "      <td>22.557983</td>\n",
       "      <td>5083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212092</th>\n",
       "      <td>36805</td>\n",
       "      <td>22:52:07</td>\n",
       "      <td>114.115402</td>\n",
       "      <td>22.558083</td>\n",
       "      <td>23:03:12</td>\n",
       "      <td>114.118484</td>\n",
       "      <td>22.547867</td>\n",
       "      <td>5084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119041</th>\n",
       "      <td>36805</td>\n",
       "      <td>23:03:45</td>\n",
       "      <td>114.118484</td>\n",
       "      <td>22.547867</td>\n",
       "      <td>23:20:09</td>\n",
       "      <td>114.133286</td>\n",
       "      <td>22.617750</td>\n",
       "      <td>5085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224103</th>\n",
       "      <td>36805</td>\n",
       "      <td>23:36:19</td>\n",
       "      <td>114.112968</td>\n",
       "      <td>22.549601</td>\n",
       "      <td>23:43:12</td>\n",
       "      <td>114.089485</td>\n",
       "      <td>22.538918</td>\n",
       "      <td>5086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170962</th>\n",
       "      <td>36805</td>\n",
       "      <td>23:46:14</td>\n",
       "      <td>114.091217</td>\n",
       "      <td>22.540768</td>\n",
       "      <td>23:53:36</td>\n",
       "      <td>114.120354</td>\n",
       "      <td>22.544300</td>\n",
       "      <td>5087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5088 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        VehicleNum     stime        slon       slat     etime        elon  \\\n",
       "427075       22396  00:19:41  114.013016  22.664818  00:23:01  114.021400   \n",
       "131301       22396  00:41:51  114.021767  22.640200  00:43:44  114.026070   \n",
       "417417       22396  00:45:44  114.028099  22.645082  00:47:44  114.030380   \n",
       "376160       22396  01:08:26  114.034897  22.616301  01:16:34  114.035614   \n",
       "21768        22396  01:26:06  114.046021  22.641251  01:34:48  114.066048   \n",
       "...            ...       ...         ...        ...       ...         ...   \n",
       "175519       36805  22:49:12  114.114365  22.550632  22:50:40  114.115501   \n",
       "212092       36805  22:52:07  114.115402  22.558083  23:03:12  114.118484   \n",
       "119041       36805  23:03:45  114.118484  22.547867  23:20:09  114.133286   \n",
       "224103       36805  23:36:19  114.112968  22.549601  23:43:12  114.089485   \n",
       "170962       36805  23:46:14  114.091217  22.540768  23:53:36  114.120354   \n",
       "\n",
       "             elat    ID  \n",
       "427075  22.663918     0  \n",
       "131301  22.640266     1  \n",
       "417417  22.650017     2  \n",
       "376160  22.646717     3  \n",
       "21768   22.636183     4  \n",
       "...           ...   ...  \n",
       "175519  22.557983  5083  \n",
       "212092  22.547867  5084  \n",
       "119041  22.617750  5085  \n",
       "224103  22.538918  5086  \n",
       "170962  22.544300  5087  \n",
       "\n",
       "[5088 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract taxi OD from GPS data\n",
    "#OD形成就是每一个订单都算一个OD行程，每一行代表一个OD行程\n",
    "#stime代表出发时间，slon和slat是出发的经纬度\n",
    "#etime代表结束时间，elon和elat是结束的经纬度\n",
    "#   ID是订单顺序号\n",
    "oddata = tbd.taxigps_to_od(data,col = ['VehicleNum', 'Time', 'Lng', 'Lat', 'OpenStatus'])\n",
    "oddata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b9d8c3da",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Lng</th>\n",
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      ],
      "text/plain": [
       "        VehicleNum      Time         Lng        Lat  OpenStatus  Speed  \\\n",
       "427075       22396  00:19:41  114.013016  22.664818           1   63.0   \n",
       "427085       22396  00:19:49  114.014030  22.665483           1   55.0   \n",
       "416622       22396  00:21:01  114.018898  22.662500           1    1.0   \n",
       "427480       22396  00:21:41  114.019348  22.662300           1    7.0   \n",
       "416623       22396  00:22:21  114.020615  22.663366           1    0.0   \n",
       "\n",
       "        LONCOL  LATCOL   ID  flag  \n",
       "427075    85.0    59.0  0.0   1.0  \n",
       "427085    85.0    59.0  0.0   1.0  \n",
       "416622    86.0    58.0  0.0   1.0  \n",
       "427480    86.0    58.0  0.0   1.0  \n",
       "416623    86.0    59.0  0.0   1.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_deliver, data_idle = tbd.taxigps_traj_point(data,oddata,col=['VehicleNum',\n",
    "                                                                  'Time',\n",
    "                                                                  'Lng',\n",
    "                                                                  'Lat',\n",
    "                                                                  'OpenStatus'])\n",
    "data_deliver.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c54ac982",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>VehicleNum</th>\n",
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       "      <th>Lng</th>\n",
       "      <th>Lat</th>\n",
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       "      <td>36805</td>\n",
       "      <td>23:53:09</td>\n",
       "      <td>114.120354</td>\n",
       "      <td>22.544300</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>32.0</td>\n",
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       "      <th>64405</th>\n",
       "      <td>36805</td>\n",
       "      <td>23:53:15</td>\n",
       "      <td>114.120354</td>\n",
       "      <td>22.544300</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5087.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>64390</th>\n",
       "      <td>36805</td>\n",
       "      <td>23:53:21</td>\n",
       "      <td>114.120354</td>\n",
       "      <td>22.544300</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5087.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>64406</th>\n",
       "      <td>36805</td>\n",
       "      <td>23:53:27</td>\n",
       "      <td>114.120354</td>\n",
       "      <td>22.544300</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5087.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>64393</th>\n",
       "      <td>36805</td>\n",
       "      <td>23:53:33</td>\n",
       "      <td>114.120354</td>\n",
       "      <td>22.544300</td>\n",
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       "      <td>32.0</td>\n",
       "      <td>5087.0</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>209250 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        VehicleNum      Time         Lng        Lat  OpenStatus  Speed  \\\n",
       "427075       22396  00:19:41  114.013016  22.664818           1   63.0   \n",
       "427085       22396  00:19:49  114.014030  22.665483           1   55.0   \n",
       "416622       22396  00:21:01  114.018898  22.662500           1    1.0   \n",
       "427480       22396  00:21:41  114.019348  22.662300           1    7.0   \n",
       "416623       22396  00:22:21  114.020615  22.663366           1    0.0   \n",
       "...            ...       ...         ...        ...         ...    ...   \n",
       "64411        36805  23:53:09  114.120354  22.544300           1    2.0   \n",
       "64405        36805  23:53:15  114.120354  22.544300           1    1.0   \n",
       "64390        36805  23:53:21  114.120354  22.544300           1    0.0   \n",
       "64406        36805  23:53:27  114.120354  22.544300           1    0.0   \n",
       "64393        36805  23:53:33  114.120354  22.544300           1    0.0   \n",
       "\n",
       "        LONCOL  LATCOL      ID  flag  \n",
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       "\n",
       "[209250 rows x 10 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_deliver #载客状态的od路径"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "53f9414e",
   "metadata": {},
   "outputs": [
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       "\n",
       "[332941 rows x 10 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_idle #空载状态的od路径"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ae29e955",
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "        VehicleNum      Time         Lng        Lat  OpenStatus  Speed  \\\n",
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       "401744       22396  00:25:01  114.027115  22.662100           0   25.0   \n",
       "394630       22396  00:25:41  114.024551  22.659834           0   21.0   \n",
       "\n",
       "        LONCOL  LATCOL   ID  flag  \n",
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     "execution_count": 25,
     "metadata": {},
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   "source": [
    "data_idle.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5114a423",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "traj_deliver = tbd.points_to_traj(data_deliver)\n",
    "traj_deliver.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "098679a7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "traj_idle = tbd.points_to_traj(data_idle[data_idle['OpenStatus'] == 0])\n",
    "traj_idle.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6d28932b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VehicleNum</th>\n",
       "      <th>Time</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Lat</th>\n",
       "      <th>OpenStatus</th>\n",
       "      <th>Speed</th>\n",
       "      <th>LONCOL</th>\n",
       "      <th>LATCOL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>452072</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:00:29</td>\n",
       "      <td>113.996719</td>\n",
       "      <td>22.693333</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>81</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444077</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:01:01</td>\n",
       "      <td>113.995514</td>\n",
       "      <td>22.695032</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>81</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444078</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:01:09</td>\n",
       "      <td>113.995430</td>\n",
       "      <td>22.695766</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>81</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444075</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:01:41</td>\n",
       "      <td>113.995369</td>\n",
       "      <td>22.696484</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444079</th>\n",
       "      <td>22396</td>\n",
       "      <td>00:02:21</td>\n",
       "      <td>113.995430</td>\n",
       "      <td>22.696650</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>81</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64390</th>\n",
       "      <td>36805</td>\n",
       "      <td>23:53:21</td>\n",
       "      <td>114.120354</td>\n",
       "      <td>22.544300</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>107</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64406</th>\n",
       "      <td>36805</td>\n",
       "      <td>23:53:27</td>\n",
       "      <td>114.120354</td>\n",
       "      <td>22.544300</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>107</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64393</th>\n",
       "      <td>36805</td>\n",
       "      <td>23:53:33</td>\n",
       "      <td>114.120354</td>\n",
       "      <td>22.544300</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>107</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64391</th>\n",
       "      <td>36805</td>\n",
       "      <td>23:53:36</td>\n",
       "      <td>114.120354</td>\n",
       "      <td>22.544300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>107</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64396</th>\n",
       "      <td>36805</td>\n",
       "      <td>23:53:51</td>\n",
       "      <td>114.120354</td>\n",
       "      <td>22.544300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>107</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>542224 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        VehicleNum      Time         Lng        Lat  OpenStatus  Speed  \\\n",
       "452072       22396  00:00:29  113.996719  22.693333           1     20   \n",
       "444077       22396  00:01:01  113.995514  22.695032           1     34   \n",
       "444078       22396  00:01:09  113.995430  22.695766           1     41   \n",
       "444075       22396  00:01:41  113.995369  22.696484           1      0   \n",
       "444079       22396  00:02:21  113.995430  22.696650           1     17   \n",
       "...            ...       ...         ...        ...         ...    ...   \n",
       "64390        36805  23:53:21  114.120354  22.544300           1      0   \n",
       "64406        36805  23:53:27  114.120354  22.544300           1      0   \n",
       "64393        36805  23:53:33  114.120354  22.544300           1      0   \n",
       "64391        36805  23:53:36  114.120354  22.544300           0      0   \n",
       "64396        36805  23:53:51  114.120354  22.544300           0      0   \n",
       "\n",
       "        LONCOL  LATCOL  \n",
       "452072      81      65  \n",
       "444077      81      66  \n",
       "444078      81      66  \n",
       "444075      81      66  \n",
       "444079      81      66  \n",
       "...        ...     ...  \n",
       "64390      107      32  \n",
       "64406      107      32  \n",
       "64393      107      32  \n",
       "64391      107      32  \n",
       "64396      107      32  \n",
       "\n",
       "[542224 rows x 8 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6944e197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User Guide: https://docs.kepler.gl/docs/keplergl-jupyter\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6205b8df3259413a8227ed6987779a87",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "KeplerGl(config={'version': 'v1', 'config': {'visState': {'filters': [], 'layers': [{'id': 'ytak0zp', 'type': …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化数据点分布\n",
    "tbd.visualization_data(data,col = ['Lng','Lat'],accuracy=40,height = 500) #accuracy是栅格大小，数值越大表示一个栅格涵盖地图面积越大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "17c1098f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User Guide: https://docs.kepler.gl/docs/keplergl-jupyter\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c5bfa6e69254ddda8eec1b167376a10",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "KeplerGl(config={'version': 'v1', 'config': {'visState': {'filters': [], 'layers': [{'id': 'ytak0zp', 'type': …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化数据点分布\n",
    "tbd.visualization_data(data,col = ['Lng','Lat'],accuracy=100,height = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8dc02ebd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ScauTheShy\\anaconda3\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py:112: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n",
      "  values = construct_1d_object_array_from_listlike(values)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User Guide: https://docs.kepler.gl/docs/keplergl-jupyter\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "59d7f13455194f2183e1bd63d4707d95",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "KeplerGl(config={'version': 'v1', 'config': {'visState': {'filters': [], 'layers': [{'id': 'd3s4dcp', 'type': …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化数据点分布\n",
    "tbd.visualization_od(oddata,accuracy=2000,height = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c15738cf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "        VehicleNum     stime        slon       slat     etime        elon  \\\n",
       "427075       22396  00:19:41  114.013016  22.664818  00:23:01  114.021400   \n",
       "131301       22396  00:41:51  114.021767  22.640200  00:43:44  114.026070   \n",
       "417417       22396  00:45:44  114.028099  22.645082  00:47:44  114.030380   \n",
       "376160       22396  01:08:26  114.034897  22.616301  01:16:34  114.035614   \n",
       "21768        22396  01:26:06  114.046021  22.641251  01:34:48  114.066048   \n",
       "...            ...       ...         ...        ...       ...         ...   \n",
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       "\n",
       "             elat    ID  SLONCOL  SLATCOL  ELONCOL  ELATCOL  count  \n",
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       "...           ...   ...      ...      ...      ...      ...    ...  \n",
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       "\n",
       "[5088 rows x 13 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oddata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0997e843",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing trajectory data...\n",
      "Generate visualization...\n",
      "User Guide: https://docs.kepler.gl/docs/keplergl-jupyter\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b73efe23379d42818f6fa026e2166039",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "KeplerGl(config={'version': 'v1', 'config': {'visState': {'filters': [], 'layers': [{'id': 'hizm36i', 'type': …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tbd.visualization_trip(data_deliver)  #一天当中的所有载客轨迹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0aaa1caf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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